Hierarchical image classification is a fundamental challenge in medical image analysis, as tree-structured taxonomies inherently reflect biological and clinical relationships, spanning the general categorisation of disease entities and fine-grained cellular distinctions. Existing approaches primarily rely on multi-task learning and fine-grained detection, often requiring intricate model design and complex training strategies. In this paper, we aim to exploit the negative curvature property of hyperbolic space, which allows efficient representation of hierarchical structures. We propose a dual-geometry image-text framework, termed H 2 CL. Specifically, we introduce a lightweight classifier head on top of image backbones to extract both Euclidean and hyperbolic features, which are then combined to simultaneously preserve taxonomic consistency from an etiological perspective and enhance instance discrimination from a morphological perspective. Furthermore, a text branch is incorporated to integrate label semantics, where an entailment loss is employed to jointly model image–text alignment and inter-sample relationships. Extensive experiments on cervical cell, skin lesion, and gallbladder disease datasets demonstrate that our framework consistently outperforms advanced methods. Compared to the standard Swin Transformer, H 2 CL achieves an average accuracy improvement of 7% across all three datasets at the fine-grained level, with similarly consistent gains observed when integrated with other backbone models. The source code is publicly available at https://github.com/MCPathology/H2CL .

• Curvi-Tracker refines curvilinear structure segmentation using intelligent tracker agents. • Novel Direction-Net and Forward-Net improve connectivity and preserve topology. • Extensive experiments demonstrate effectiveness of the proposed Curvi-Tracker. Curvilinear structures are ubiquitous in various domains, such as blood vessels in medical images or roads in satellite images. The automation of curvilinear structure segmentation is highly beneficial because of the laborious and error-prone process of manual annotation. Existing methods produce segmentation results with decent pixel-level performance, but still with presence of incorrect connectivity. To overcome the challenge, this paper proposes Curvi-Tracker, a novel refinement framework that improves initial coarse segmentation results by deploying tracker agents on detected foreground pixels. The proposed framework has two main components: a Direction-Net and a Forward-Net, which jointly guide the movement of trackers in order to track the curvilinear object. A Direction-Aware Multi-Label loss and a Stepwise Masked loss are proposed for accurate tracking of curvilinear structures. Experiments on public datasets of various curvilinear objects including retinal vessels, roads and pavement cracks demonstrate that the proposed method consistently improves the topological correctness of coarse segmentation results coarse segmentation results, averaging overall 10 % of improvement in all three topological metrics.

Monocular 3D human pose estimation often faces challenges due to depth ambiguities, unstable predictions, and occlusions. Diffusion models have emerged as a generative framework that can transform noise into complex data representations. Although diffusion-based multi-hypothesis approaches have been explored in prior works, their effectiveness largely depends on the denoising network, which captures spatial and long-range temporal dependencies. In this paper, we propose MHDPose, a conditional diffusion-based human pose estimation framework that can generate multiple 3D pose predictions with the guidance of a single 2D pose. The pose hypotheses are generated by the proposed PMamba denoiser that consists of: (i) a spatial transformer over joints with proposed kinematic-aware rotary position embeddings (Kin-RoPE) to encode the skeleton’s tree structure, and (ii) bi-directional Mamba blocks for efficient long-range temporal modeling, and a residual Mamba head for pose refinement. Our proposed MHDPose achieves competitive results on widely used pose estimation benchmarks such as Human3.6M and MPI-INF-3DHP.

Zero-shot learning (ZSL) offers tremendous potential for histopathology image analysis, enabling models to generalize to unseen classes without extensive labeled data. Recent vision-language model (VLM) advancements have expanded ZSL capabilities, allowing task performance without task-specific fine-tuning. However, applying VLMs to histopathology presents considerable challenges due to the complexity of histopathological imagery and the nuanced nature of diagnostic tasks. We propose Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE), a novel framework for zero-shot histopathology image classification. MR-PHE mimics pathologists' workflow through multiresolution patch extraction to capture key cellular and tissue features. It introduces a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings, effectively combining local and global contextual information. Additionally, we develop a comprehensive prompt generation and selection framework, enriching class descriptions with domain-specific synonyms and clinically relevant features to enhance semantic understanding. A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings, emphasizing diagnostically important regions during classification. Experimental results demonstrate MR-PHE significantly improves zero-shot classification performance on histopathology datasets, often surpassing fully supervised models, showing its effectiveness and potential to advance computational pathology.

Unsupervised domain adaptation (UDA) addresses the domain shift problem by transferring knowledge from labeled source domain data (e.g. CT) to unlabeled target domain data (e.g. MRI). While state-of-the-art methods reduce domain gaps via image- or feature-level alignment, their reliance on spurious correlations in the training data often limits generalization across domains. To overcome this limitation, we propose the Causal Intervention Segmentation Network (CiSeg), a novel framework that first integrates causal inference into UDA. A Structural Causal Model (SCM) is first constructed for the source domain to disentangle causal variables from bias variables, alleviating the impact of spurious correlations. Based on this SCM, we introduce a Counterfactual Disentanglement (CD) module to decompose the source domain's latent features into distinct causal and bias components, effectively eliminating their mutual dependencies. To enhance cross-domain consistency, two auxiliary components are introduced: Prototype-guided Contrastive Learning (PCL) and Causal-bias Residual Alignment (CBRA). PCL aligns pixel-level representations with their corresponding semantic prototypes, promoting stronger intra-class consistency and clearer inter-class separability. CBRA employs adversarial learning to align causal and bias residual features across domains, further enhancing feature-level invariance. Extensive experiments on cardiac, abdominal multi-organ, and BraTS18 segmentation tasks demonstrate that CiSeg outperforms state-of-the-art methods, achieving superior segmentation performance and robust cross-domain generalization. Code and models are available at https://github.com/lvpeiqing/CiSeg.

Unsupervised anomaly detection (UAD) methods typically detect anomalies by learning and reconstructing the normative distribution. However, since anomalies constantly invade and affect their surroundings, sub-healthy areas in the junction present structural deformations that could be easily misidentified as anomalies, posing difficulties for UAD methods that solely learn the normative distribution. The use of multimodal images can facilitate to address the above challenges, as they can provide complementary information of anomalies. Therefore, this paper propose a novel method for UAD in preoperative multimodal images, called Erasure Perception Diffusion model (EPDiff). First, the Local Erasure Progressive Training (LEPT) framework is designed to better rebuild sub-healthy structures around anomalies through the diffusion model with a two-phase process. Initially, healthy images are used to capture deviation features labeled as potential anomalies. Then, these anomalies are locally erased in multimodal images to progressively learn sub-healthy structures, obtaining a more detailed reconstruction around anomalies. Second, the Global Structural Perception (GSP) module is developed in the diffusion model to realize global structural representation and correlation within images and between modalities through interactions of high-level semantic information. In addition, a training-free module, named Multimodal Attention Fusion (MAF) module, is presented for weighted fusion of anomaly maps between different modalities and obtaining binary anomaly outputs. Experimental results show that EPDiff improves the AUPRC and mDice scores by 2% and 3.9% on BraTS2021, and by 5.2% and 4.5% on Shifts over the state-of-the-art methods, which proves the applicability of EPDiff in diverse anomaly diagnosis. The code is available at https://github.com/wjiazheng/EPDiff.

Change detection (CD) in remote sensing is a crucial yet challenging task, particularly due to the labor-intensive nature of labeling bi-temporal images. We introduce a novel framework that leverages synthetic datasets, style transfer, and semi-supervised learning to enhance CD model performance while reducing the dependency on labeled data. Our approach begins with a GAN-based style transfer model that transforms synthetic images to align with real-world scenarios, narrowing the domain gap. These transformed images, combined with a small amount of labeled real data, are used for supervised training to build a robust initial model. We then apply a mean teacher model to integrate unlabeled real images, allowing for effective semi-supervised learning. Our method achieves state-of-the-art performance on the LEVIR and WHU-CD datasets, demonstrating its robustness and accuracy across diverse geographical and temporal conditions.

Continual test-time adaptation (CTTA) aims to continuously adapt a source-trained model to a target domain with minimal performance loss while assuming no access to the source data. Typically, source models are trained with empirical risk minimization (ERM) and assumed to perform reasonably on the target domain to allow for further adaptation. However, ERM-trained models often fail to perform adequately on a severely drifted target domain, resulting in unsatisfactory adaptation results. To tackle this issue, we propose a generalizable CTTA framework. First, we incorporate domain-invariant shape modeling into the model and train it using domain-generalization (DG) techniques, promoting target-domain adaptability regardless of the severity of the domain shift. Then, an uncertainty and shape-aware mean teacher network performs adaptation with uncertainty-weighted pseudo-labels and shape information. As part of this process, a novel uncertainty-ranked cross-task regularization scheme is proposed to impose consistency between segmentation maps and their corresponding shape representations, both produced by the student model, at the patch and global levels to enhance performance further. Lastly, small portions of the model's weights are stochastically reset to the initial domain-generalized state at each adaptation step, preventing the model from 'diving too deep' into any specific test samples. The proposed method demonstrates strong continual adaptability and outperforms its peers on five cross-domain segmentation tasks, showcasing its effectiveness and generalizability.

Abstract Airway segmentation is essential in computer-assisted diagnosis and screening of bronchial diseases due to the inherent difficulty in obtaining a direct and clear visualization of airway trees from raw CT images. Although medical image segmentation technology is gradually maturing and beginning to be applied in clinics, challenges like breakages and leakages remain in airway segmentation. We propose a novel framework that enhances UNet3D with large-kernel attention for improved global and local feature extraction. A multitask prediction head across voxel, neighborhood, and surface scopes is introduced to better capture airway topology, supervised by customized loss functions. Additionally, a conditional updating strategy leverages a shared encoder and dual decoders to improve segmentation of thin branches by balancing over- and under-segmentation. Specifically, one decoder is optimized with hard examples to encourage over-segmentation, and the other refines results for accurate segmentation using all samples. Our model is quantitatively evaluated on the Binary Airway Segmentation dataset, achieving a Dice score of 0.904, precision of 0.954, tree detection rate of 0.950, and branch detection rate of 0.915, outperforming several recent methods in topological accuracy. In the Airway Tree Modeling Challenge 2022 validation set, our method ranks second overall by a mean position score across all metrics. Our future work aims to enhance prediction confidence and adaptability in ambiguous regions and improve generalizability and interpretability across diverse clinical datasets.

Multimodal brain tumor segmentation (BraTS), integrated with surgical robots and navigation systems, enables accurate surgical interventions while maximizing the preservation of surrounding healthy brain tissue. However, multimodal brain scans suffer from large interclass differences in brain tumor subregions and information redundancy, leading to inadequate fusion of multimodal information and significantly affecting the accuracy of BraTS. To address the above problems, we propose a multibranch encoder aggregation (MEA) network based on a layer-fusion strategy called multibranch UNeXt (MBUNeXt). The network comprises three well-designed modules: the multimodal feature attention (MFA) module, the MEA module, and the large-kernel convolution skip (LCS)-connection module. These modules work together to achieve precise segmentation of brain tumors. Specifically, the MFA module preserves the intermodality similarity structure through attention mechanisms and Gaussian modulation functions, thereby filtering redundant information. Then, the MEA module exploits the correlations among multiple modalities to effectively integrate multimodal hybrid feature representation and optimize multimodal information fusion. In addition, the LCS module constructs multiple groups of depthwise separable convolutions with large kernel, which can guide the network to attend to features at different scales, thereby addressing the issue of significant interclass differences in brain tumor subregions. The experimental results on the large-scale public datasets, BraTS2019 and BraTS2021, which consist of approximately 5000 3-D brain scans, demonstrate that our proposed method has achieved SOTA performance, with average Dice scores of 85.84% and 91.11%, respectively. It also performs well on the BraTS-Africa2024 dataset with low imaging quality, confirming its robustness. The code is available at https://github.com/liuqinghao2018/MBUNeXt.

Retinal diseases can cause irreversible vision loss in both eyes if not diagnosed and treated early. Since retinal diseases are so complicated, retinal imaging is likely to show two or more abnormalities. Current deep learning techniques for segmenting retinal images with many labels and attributes have poor detection accuracy and generalisability. This paper presents a multipath convolutional neural network for multifeature segmentation. The proposed network is lightweight and spatially sensitive to information. A patch-based implementation is used to extract local image features, and focal modulation attention blocks are incorporated between the encoder and the decoder for improved segmentation. Filter optimisation is used to prevent filter overlaps and speed up model convergence. A combination of convolution operations and group convolution operations is used to reduce computational costs. This is the first robust and generalisable network capable of segmenting multiple features of fundus images (including retinal vessels, microaneurysms, optic discs, haemorrhages, hard exudates, and soft exudates). The results of our experimental evaluation on more than ten publicly available datasets with multiple features show that the proposed network outperforms recent networks despite having a small number of learnable parameters.

Bronchoscopy reveals air passages and internal tissues for accurate diagnosis of various lung diseases. Robot-assisted bronchoscopy using an airway tree model can help path planning before surgery and navigation during surgery. In airway tree modeling, though volumetric deep learning methods have achieved good performance for airway segmentation, it remains a challenge due to the breakages and leakages. Some existing methods adopt post-processing using traditional methods like morphological and fuzzy connected algorithms. Also, some methods convert the volumetric data to point cloud format to refine segmentation. In this paper, we develop a new point cloud-based approach to refine volumetric segmentation. To address the breakage issue, we approach it as a regression problem of the branch extension direction and length. To tackle the leakage issue, we approach it as a segmentation task to eliminate leakages caused by breakage filling and from volumetric segmentation. Moreover, the direction information of branches is crucial for constructing the airway tree while point clouds do not naturally encode it. To introduce this information, we propose a directional feature aggregation, which first decomposes features of neighboring points based on their locations and aggregates decomposed features to aid the network in capturing the directional information effectively. Our proposed model has been evaluated on two public datasets, and the results show that our refinement can improve the volumetric segmentation.

Segmentation is an important task in a wide range of computer vision applications, including medical image analysis. Recent years have seen an increase in the complexity of medical image segmentation approaches based on sophisticated convolutional neural network architectures. This progress has led to incremental enhancements in performance on widely recognised benchmark datasets. However, most of the existing approaches are computationally demanding, which limits their practical applicability. This paper presents an expand-squeeze dual multiscale residual network (ESDMR-Net), which is a full y convolutional network that is particularly well-suited for resource-constrained computing hardware such as mobile devices. ESDMR-Net focusses on extracting multiscale features, enabling the learning of contextual dependencies among semantically distinct features. The ESDMR-Net architecture allows dual-stream information flow within encoder–decoder pairs. The expansion operation (depthwise separable convolution) makes all of the rich features with multiscale information available to the squeeze operation (bottleneck layer), which then extracts the necessary information for the segmentation task. The Expand-Squeeze (ES) block helps the network pay more attention to under-represented classes, which contributes to improved segmentation accuracy. To enhance the flow of information across multiple resolutions or scales, we integrated dual multiscale residual (DMR) blocks into the skip connection. This integration enables the decoder to access features from various levels of abstraction, ultimately resulting in more comprehensive feature representations. We present experiments on seven datasets from five distinct examples of applications: segmentation of retinal vessels (2×), skin lesions (2×), digestive tract polyps, lung regions, and cells. Our model demonstrates strong performance, with an F1 score of 0.8287%, 0.8211%, 0.9034%, 0.9451%, 0.9543%, 0.9840%, and 0.8424% on the DRIVE, CHASE, ISIC2017, ISIC2016, CVC-ClinicDB, MC and MoNuSeg datasets, respectively. Remarkably, our model achieves these results despite having significantly fewer trainable parameters, with a reduction of two or even three orders of magnitude.

Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile and achieve survival risk stratification of ER+ breast cancer patients. We employ multiple self-supervised feature extractors (DINO and MoCoV3) pretrained on histopathological patches to capture detailed image features. These features are then fused by a variational autoencoder and fed to a self-attention network generating patient-level features. A co-dual-cross-attention mechanism combines the histopathological features with genetic data, enabling the model to capture the interplay between them. Additionally, clinical data is incorporated using a feed-forward network, further enhancing predictive performance and achieving comprehensive multimodal feature integration. Furthermore, we introduce a weighted Cox loss function, specifically designed to handle imbalanced survival data, which is a common challenge. Our model achieves a mean concordance index of 0.77 and a time-dependent area under the curve of 0.84, outperforming state-of-the-art methods. It predicts risk (high versus low) with prognostic significance for overall survival in univariate analysis (HR=2.99, 95% CI: 1.88-4.78, p 0.005), and maintains independent significance in multivariate analysis incorporating standard clinicopathological variables (HR=2.91, 95% CI: 1.80-4.68, p 0.005).

Surgical scene segmentation is a critical task in Robotic-assisted surgery. However, the complexity of the surgical scene, which mainly includes local feature similarity (e.g., between different anatomical tissues), intraoperative complex artifacts, and indistinguishable boundaries, poses significant challenges to accurate segmentation. To tackle these problems, we propose the Long Strip Kernel Attention network (LSKANet), including two well-designed modules named Dual-block Large Kernel Attention module (DLKA) and Multiscale Affinity Feature Fusion module (MAFF), which can implement precise segmentation of surgical images. Specifically, by introducing strip convolutions with different topologies (cascaded and parallel) in two blocks and a large kernel design, DLKA can make full use of region- and strip-like surgical features and extract both visual and structural information to reduce the false segmentation caused by local feature similarity. In MAFF, affinity matrices calculated from multiscale feature maps are applied as feature fusion weights, which helps to address the interference of artifacts by suppressing the activations of irrelevant regions. Besides, the hybrid loss with Boundary Guided Head (BGH) is proposed to help the network segment indistinguishable boundaries effectively. We evaluate the proposed LSKANet on three datasets with different surgical scenes. The experimental results show that our method achieves new state-of-the-art results on all three datasets with improvements of 2.6%, 1.4%, and 3.4% mIoU, respectively. Furthermore, our method is compatible with different backbones and can significantly increase their segmentation accuracy. Code is available at https://github.com/YubinHan73/LSKANet.

Accurate morphological reconstruction of neurons in whole brain images is critical for brain science research. However, due to the wide range of whole brain imaging, uneven staining, and optical system fluctuations, there are significant differences in image properties between different regions of the ultrascale brain image, such as dramatically varying voxel intensities and inhomogeneous distribution of background noise, posing an enormous challenge to neuron reconstruction from whole brain images. In this paper, we propose an adaptive dual-task learning network (ADTL-Net) to quickly and accurately extract neuronal structures from ultrascale brain images. Specifically, this framework includes an External Features Classifier (EFC) and a Parameter Adaptive Segmentation Decoder (PASD), which share the same Multi-Scale Feature Encoder (MSFE). MSFE introduces an attention module named Channel Space Fusion Module (CSFM) to extract structure and intensity distribution features of neurons at different scales for addressing the problem of anisotropy in 3D space. Then, EFC is designed to classify these feature maps based on external features, such as foreground intensity distributions and image smoothness, and select specific PASD parameters to decode them of different classes to obtain accurate segmentation results. PASD contains multiple sets of parameters trained by different representative complex signal-to-noise distribution image blocks to handle various images more robustly. Experimental results prove that compared with other advanced segmentation methods for neuron reconstruction, the proposed method achieves state-of-the-art results in the task of neuron reconstruction from ultrascale brain images, with an improvement of about 49% in speed and 12% in F1 score.

The precise segmentation of medical images is one of the key challenges in pathology research and clinical practice. However, many medical image segmentation tasks have problems such as large differences between different types of lesions and similar shapes as well as colors between lesions and surrounding tissues, which seriously affects the improvement of segmentation accuracy. In this article, a novel method called Swin Pyramid Aggregation network (SwinPA-Net) is proposed by combining two designed modules with Swin Transformer to learn more powerful and robust features. The two modules, named dense multiplicative connection (DMC) module and local pyramid attention (LPA) module, are proposed to aggregate the multiscale context information of medical images. The DMC module cascades the multiscale semantic feature information through dense multiplicative feature fusion, which minimizes the interference of shallow background noise to improve the feature expression and solves the problem of excessive variation in lesion size and type. Moreover, the LPA module guides the network to focus on the region of interest by merging the global attention and the local attention, which helps to solve similar problems. The proposed network is evaluated on two public benchmark datasets for polyp segmentation task and skin lesion segmentation task as well as a clinical private dataset for laparoscopic image segmentation task. Compared with existing state-of-the-art (SOTA) methods, the SwinPA-Net achieves the most advanced performance and can outperform the second-best method on the mean Dice score by 1.68%, 0.8%, and 1.2% on the three tasks, respectively.

Deep neural network (DNN) models for retinopathy have estimated predictive accuracies in the mid-to-high 90%. However, the following aspects remain unaddressed: State-of-the-art models are complex and require substantial computational infrastructure to train and deploy; The reliability of predictions can vary widely. In this paper, we focus on these aspects and propose a form of iterative knowledge distillation (IKD), called IKD+ that incorporates a tradeoff between size, accuracy and reliability. We investigate the functioning of IKD+ using two widely used techniques for estimating model calibration (Platt-scaling and temperature-scaling), using the best-performing model available, which is an ensemble of EfficientNets with approximately 100M parameters. We demonstrate that IKD+ equipped with temperature-scaling results in models that show up to approximately 500-fold decreases in the number of parameters than the original ensemble without a significant loss in accuracy. In addition, calibration scores (reliability) for the IKD+ models are as good as or better than the base model.

MOTIVATION: Subcellular localization of human proteins is essential to comprehend their functions and roles in physiological processes, which in turn helps in diagnostic and prognostic studies of pathological conditions and impacts clinical decision-making. Since proteins reside at multiple locations at the same time and few subcellular locations host far more proteins than other locations, the computational task for their subcellular localization is to train a multilabel classifier while handling data imbalance. In imbalanced data, minority classes are underrepresented, thus leading to a heavy bias towards the majority classes and the degradation of predictive capability for the minority classes. Furthermore, data imbalance in multilabel settings is an even more complex problem due to the coexistence of majority and minority classes. RESULTS: Our studies reveal that based on the extent of concurrence of majority and minority classes, oversampling of minority samples through appropriate data augmentation techniques holds promising scope for boosting the classification performance for the minority classes. We measured the magnitude of data imbalance per class and the concurrence of majority and minority classes in the dataset. Based on the obtained values, we identified minority and medium classes, and a new oversampling method is proposed that includes non-linear mixup, geometric and colour transformations for data augmentation and a sampling approach to prepare minibatches. Performance evaluation on the Human Protein Atlas Kaggle challenge dataset shows that the proposed method is capable of achieving better predictions for minority classes than existing methods. AVAILABILITY AND IMPLEMENTATION: Data used in this study are available at https://www.kaggle.com/competitions/human-protein-atlas-image-classification/data. Source code is available at https://github.com/priyarana/Protein-subcellular-localisation-method. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Histopathological Whole Slide Images (WSIs) at giga-pixel resolution are the gold standard for cancer analysis and prognosis. Due to the scarcity of pixel- or patch-level annotations of WSIs, many existing methods attempt to predict survival outcomes based on a three-stage strategy that includes patch selection, patch-level feature extraction and aggregation. However, the patch features are usually extracted by using truncated models (e.g. ResNet) pretrained on ImageNet without fine-tuning on WSI tasks, and the aggregation stage does not consider the many-to-one relationship between multiple WSIs and the patient. In this paper, we propose a novel survival prediction framework that consists of patch sampling, feature extraction and patient-level survival prediction. Specifically, we employ two kinds of self-supervised learning methods, i.e. colorization and cross-channel, as pretext tasks to train convnet-based models that are tailored for extracting features from WSIs. Then, at the patient-level survival prediction we explicitly aggregate features from multiple WSIs, using consistency and contrastive losses to normalize slide-level features at the patient level. We conduct extensive experiments on three large-scale datasets: TCGA-GBM, TCGA-LUSC and NLST. Experimental results demonstrate the effectiveness of our proposed framework, as it achieves state-of-the-art performance in comparison with previous studies, with concordance index of 0.670, 0.679 and 0.711 on TCGA-GBM, TCGA-LUSC and NLST, respectively.

Surgical instrument segmentation is of great significance to robot-assisted surgery, but the noise caused by reflection, water mist, and motion blur during the surgery as well as the different forms of surgical instruments would greatly increase the difficulty of precise segmentation. A novel method called Branch Aggregation Attention network (BAANet) is proposed to address these challenges, which adopts a lightweight encoder and two designed modules, named Branch Balance Aggregation module (BBA) and Block Attention Fusion module (BAF), for efficient feature localization and denoising. By introducing the unique BBA module, features from multiple branches are balanced and optimized through a combination of addition and multiplication to complement strengths and effectively suppress noise. Furthermore, to fully integrate the contextual information and capture the region of interest, the BAF module is proposed in the decoder, which receives adjacent feature maps from the BBA module and localizes the surgical instruments from both global and local perspectives by utilizing a dual branch attention mechanism. According to the experimental results, the proposed method has the advantage of being lightweight while outperforming the second-best method by 4.03%, 1.53%, and 1.34% in mIoU scores on three challenging surgical instrument datasets, respectively, compared to the existing state-of-the-art methods. Code is available at https://github.com/SWT-1014/BAANet.

3D soma detection in whole brain images is a critical step for neuron reconstruction. However, existing soma detection methods are not suitable for whole mouse brain images with large amounts of data and complex structure. In this paper, we propose a two-stage deep neural network to achieve fast and accurate soma detection in large-scale and high-resolution whole mouse brain images (more than 1TB). For the first stage, a lightweight Multi-level Cross Classification Network (MCC-Net) is proposed to filter out images without somas and generate coarse candidate images by combining the advantages of the multi convolution layer's feature extraction ability. It can speed up the detection of somas and reduce the computational complexity. For the second stage, to further obtain the accurate locations of somas in the whole mouse brain images, the Scale Fusion Segmentation Network (SFS-Net) is developed to segment soma regions from candidate images. Specifically, the SFS-Net captures multi-scale context information and establishes a complementary relationship between encoder and decoder by combining the encoder-decoder structure and a 3D Scale-Aware Pyramid Fusion (SAPF) module for better segmentation performance. The experimental results on three whole mouse brain images verify that the proposed method can achieve excellent performance and provide the reconstruction of neurons with beneficial information. Additionally, we have established a public dataset named WBMSD, including 798 high-resolution and representative images ( 256 ×256 ×256 voxels) from three whole mouse brain images, dedicated to the research of soma detection, which will be released along with this paper.

Microscopy cell segmentation is a crucial step in biological image analysis and a challenging task. In recent years, deep learning has been widely used to tackle this task, with promising results. A critical aspect of training complex neural networks for this purpose is the selection of the loss function, as it affects the learning process. In the field of cell segmentation, most of the recent research in improving the loss function focuses on addressing the problem of inter-class imbalance. Despite promising achievements, more work is needed, as the challenge of cell segmentation is not only the inter-class imbalance but also the intra-class imbalance (the cost imbalance between the false positives and false negatives of the inference model), the segmentation of cell minutiae, and the missing annotations. To deal with these challenges, in this paper, we propose a new compound loss function employing a shape aware weight map. The proposed loss function is inspired by Youden's J index to handle the problem of inter-class imbalance and uses a focal cross-entropy term to penalize the intra-class imbalance and weight easy/hard samples. The proposed shape aware weight map can handle the problem of missing annotations and facilitate valid segmentation of cell minutiae. Results of evaluations on all ten 2D+time datasets from the public cell tracking challenge demonstrate 1) the superiority of the proposed loss function with the shape aware weight map, and 2) that the performance of recent deep learning-based cell segmentation methods can be improved by using the proposed compound loss function.

Precise quantification of tree-like structures from biomedical images, such as neuronal shape reconstruction and retinal blood vessel caliber estimation, is increasingly important in understanding normal function and pathologic processes in biology. Some handcrafted methods have been proposed for this purpose in recent years. However, they are designed only for a specific application. In this paper, we propose a shape analysis algorithm, DeepRayburst, that can be applied to many different applications based on a Multi-Feature Rayburst Sampling (MFRS) and a Dual Channel Temporal Convolutional Network (DC-TCN). Specifically, we first generate a Rayburst Sampling (RS) core containing a set of multidirectional rays. Then the MFRS is designed by extending each ray of the RS to multiple parallel rays which extract a set of feature sequences. A Gaussian kernel is then used to fuse these feature sequences and outputs one feature sequence. Furthermore, we design a DC-TCN to make the rays terminate on the surface of tree-like structures according to the fused feature sequence. Finally, by analyzing the distribution patterns of the terminated rays, the algorithm can serve multiple shape analysis applications of tree-like structures. Experiments on three different applications, including soma shape reconstruction, neuronal shape reconstruction, and vessel caliber estimation, confirm that the proposed method outperforms other state-of-the-art shape analysis methods, which demonstrate its flexibility and robustness.

Digital reconstruction of neuronal structures from 3D microscopy images is critical for the quantitative investigation of brain circuits and functions. It is a challenging task that would greatly benefit from automatic neuron reconstruction methods. In this paper, we propose a novel method called SPE-DNR that combines spherical-patches extraction (SPE) and deep-learning for neuron reconstruction (DNR). Based on 2D Convolutional Neural Networks (CNNs) and the intensity distribution features extracted by SPE, it determines the tracing directions and classifies voxels into foreground or background. This way, starting from a set of seed points, it automatically traces the neurite centerlines and determines when to stop tracing. To avoid errors caused by imperfect manual reconstructions, we develop an image synthesizing scheme to generate synthetic training images with exact reconstructions. This scheme simulates 3D microscopy imaging conditions as well as structural defects, such as gaps and abrupt radii changes, to improve the visual realism of the synthetic images. To demonstrate the applicability and generalizability of SPE-DNR, we test it on 67 real 3D neuron microscopy images from three datasets. The experimental results show that the proposed SPE-DNR method is robust and competitive compared with other state-of-the-art neuron reconstruction methods.

Digital morphology reconstruction from neuron volumetric images is essential for computational neuroscience. The centerline of the axonal and dendritic tree provides an effective shape representation and serves as a basis for further neuron reconstruction. However, it is still a challenge to directly extract the accurate centerline from the complex neuron structure with poor image quality. In this paper, we propose a neuron centerline extraction method based on a 3D tubular flux model via a two-stage CNN framework. In the first stage, a 3D CNN is used to learn the latent neuron structure features, namely flux features, from neuron images. In the second stage, a light-weight U-Net takes the learned flux features as input to extract the centerline with a spatial weighted average strategy to constrain the multi-voxel width response. Specifically, the labels of flux features in the first stage are generated by the 3D tubular model which calculates the geometric representations of the flux between each voxel in the tubular region and the nearest point on the centerline ground truth. Compared with self-learned features by networks, flux features, as a kind of prior knowledge, explicitly take advantage of the contextual distance and direction distribution information around the centerline, which is beneficial for the precise centerline extraction. Experiments on two challenging datasets demonstrate that the proposed method outperforms other state-of-the-art methods by 18% and 35.1% in F1-measurement and average distance scores at the most, and the extracted centerline is helpful to improve the neuron reconstruction performance.

Digital reconstruction of neuronal morphologies in 3D microscopy images is critical in the field of neuroscience. However, most existing automatic tracing algorithms cannot obtain accurate neuron reconstruction when processing 3D neuron images contaminated by strong background noises or containing weak filament signals. In this paper, we present a 3D neuron segmentation network named Structure-Guided Segmentation Network (SGSNet) to enhance weak neuronal structures and remove background noises. The network contains a shared encoding path but utilizes two decoding paths called Main Segmentation Branch (MSB) and Structure-Detection Branch (SDB), respectively. MSB is trained on binary labels to acquire the 3D neuron image segmentation maps. However, the segmentation results in challenging datasets often contain structural errors, such as discontinued segments of the weak-signal neuronal structures and missing filaments due to low signal-to-noise ratio (SNR). Therefore, SDB is presented to detect the neuronal structures by regressing neuron distance transform maps. Furthermore, a Structure Attention Module (SAM) is designed to integrate the multi-scale feature maps of the two decoding paths, and provide contextual guidance of structural features from SDB to MSB to improve the final segmentation performance. In the experiments, we evaluate our model in two challenging 3D neuron image datasets, the BigNeuron dataset and the Extended Whole Mouse Brain Sub-image (EWMBS) dataset. When using different tracing methods on the segmented images produced by our method rather than other state-of-the-art segmentation methods, the distance scores gain 42.48% and 35.83% improvement in the BigNeuron dataset and 37.75% and 23.13% in the EWMBS dataset.

MOTIVATION: Live cell segmentation is a crucial step in biological image analysis and is also a challenging task because time-lapse microscopy cell sequences usually exhibit complex spatial structures and complicated temporal behaviors. In recent years, numerous deep learning-based methods have been proposed to tackle this task and obtained promising results. However, designing a network with excellent performance requires professional knowledge and expertise and is very time-consuming and labor-intensive. Recently emerged neural architecture search (NAS) methods hold great promise in eliminating these disadvantages, because they can automatically search an optimal network for the task. RESULTS: We propose a novel NAS-based solution for deep learning-based cell segmentation in time-lapse microscopy images. Different from current NAS methods, we propose (i) jointly searching non-repeatable micro architectures to construct the macro network for exploring greater NAS potential and better performance and (ii) defining a specific search space suitable for the live cell segmentation task, including the incorporation of a convolutional long short-term memory network for exploring the temporal information in time-lapse sequences. Comprehensive evaluations on the 2D datasets from the cell tracking challenge demonstrate the competitiveness of the proposed method compared to the state of the art. The experimental results show that the method is capable of achieving more consistent top performance across all ten datasets than the other challenge methods. AVAILABILITYAND IMPLEMENTATION: The executable files of the proposed method as well as configurations for each dataset used in the presented experiments will be available for non-commercial purposes from https://github.com/291498346/nas_cellseg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detection and localization of terminations and junctions is a key step in the morphological reconstruction of tree-like structures in images. Previously, a ray-shooting model was proposed to detect termination points automatically. In this paper, we propose an automatic method for 3D junction points detection in biomedical images, relying on a circular sampling model and a 2D-to-3D reverse mapping approach. First, the existing ray-shooting model is improved to a circular sampling model to extract the pixel intensity distribution feature across the potential branches around the point of interest. The computation cost can be reduced dramatically compared to the existing ray-shooting model. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed to detect 2D junction points in maximum intensity projections (MIPs) of sub-volume images in a given 3D image, by determining the number of branches in the candidate junction region. Further, a 2D-to-3D reverse mapping approach is used to map these detected 2D junction points in MIPs to the 3D junction points in the original 3D images. The proposed 3D junction point detection method is implemented as a build-in tool in the Vaa3D platform. Experiments on multiple 2D images and 3D images show average precision and recall rates of 87.11% and 88.33% respectively. In addition, the proposed algorithm is dozens of times faster than the existing deep-learning based model. The proposed method has excellent performance in both detection precision and computation efficiency for junction detection even in large-scale biomedical images.

Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of good seed points. 3D neuron critical points, including terminations, branch points and cross-over points, are good candidates for such seed points. However, a method that can simultaneously detect all types of critical points has barely been explored. In this work, we present a method to simultaneously detect all 3 types of 3D critical points in neuron microscopy images, based on a spherical-patches extraction (SPE) method and a 2D multi-stream convolutional neural network (CNN). SPE uses a set of concentric spherical surfaces centered at a given critical point candidate to extract intensity distribution features around the point. Then, a group of 2D spherical patches is generated by projecting the surfaces into 2D rectangular image patches according to the orders of the azimuth and the polar angles. Finally, a 2D multi-stream CNN, in which each stream receives one spherical patch as input, is designed to learn the intensity distribution features from those spherical patches and classify the given critical point candidate into one of four classes: termination, branch point, cross-over point or non-critical point. Experimental results confirm that the proposed method outperforms other state-of-the-art critical points detection methods. The critical points based neuron reconstruction results demonstrate the potential of the detected neuron critical points to be good seed points for neuron reconstruction. Additionally, we have established a public dataset dedicated for neuron critical points detection, which has been released along with this article.

The morphology reconstruction (tracing) of neurons in 3D microscopy images is important to neuroscience research. However, this task remains very challenging because of the low signal-to-noise ratio (SNR) and the discontinued segments of neurite patterns in the images. In this paper, we present a neuronal structure segmentation method based on the ray-shooting model and the Long Short-Term Memory (LSTM)-based network to enhance the weak-signal neuronal structures and remove background noise in 3D neuron microscopy images. Specifically, the ray-shooting model is used to extract the intensity distribution features within a local region of the image. And we design a neural network based on the dual channel bidirectional LSTM (DC-BLSTM) to detect the foreground voxels according to the voxel-intensity features and boundary-response features extracted by multiple ray-shooting models that are generated in the whole image. This way, we transform the 3D image segmentation task into multiple 1D ray/sequence segmentation tasks, which makes it much easier to label the training samples than many existing Convolutional Neural Network (CNN) based 3D neuron image segmentation methods. In the experiments, we evaluate the performance of our method on the challenging 3D neuron images from two datasets, the BigNeuron dataset and the Whole Mouse Brain Sub-image (WMBS) dataset. Compared with the neuron tracing results on the segmented images produced by other state-of-the-art neuron segmentation methods, our method improves the distance scores by about 32% and 27% in the BigNeuron dataset, and about 38% and 27% in the WMBS dataset.

MOTIVATION: Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data. RESULTS: Here, we present a deep-learning-based method for the data association stage of particle tracking. The proposed method uses convolutional neural networks and long short-term memory networks to extract relevant dynamics features and predict the motion of a particle and the cost of linking detected particles from one time point to the next. Comprehensive evaluations on datasets from the particle tracking challenge demonstrate the competitiveness of the proposed deep-learning method compared to the state of the art. Additional tests on real-time-lapse fluorescence microscopy images of various types of intracellular particles show the method performs comparably with human experts. AVAILABILITY AND IMPLEMENTATION: The software code implementing the proposed method as well as a description of how to obtain the test data used in the presented experiments will be available for non-commercial purposes from https://github.com/yoyohoho0221/pt_linking. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The recognition of different cell compartments, the types of cells, and their interactions is a critical aspect of quantitative cell biology. However, automating this problem has proven to be non-trivial and requires solving multi-class image segmentation tasks that are challenging owing to the high similarity of objects from different classes and irregularly shaped structures. To alleviate this, graphical models are useful due to their ability to make use of prior knowledge and model inter-class dependences. Directed acyclic graphs, such as trees, have been widely used to model top-down statistical dependences as a prior for improved image segmentation. However, using trees, a few inter-class constraints can be captured. To overcome this limitation, we propose polytree graphical models that capture label proximity relations more naturally compared to tree-based approaches. A novel recursive mechanism based on two-pass message passing was developed to efficiently calculate closed-form posteriors of graph nodes on polytrees. The algorithm is evaluated on simulated data and on two publicly available fluorescence microscopy datasets, outperforming directed trees and three state-of-the-art convolutional neural networks, namely, SegNet, DeepLab, and PSPNet. Polytrees are shown to outperform directed trees in predicting segmentation error by highlighting areas in the segmented image that do not comply with prior knowledge. This paves the way to uncertainty measures on the resulting segmentation and guide subsequent segmentation refinement.

We present a 3D bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-lapse confocal microscopy image data. Our workflow makes use of convolutional neural networks trained using real as well as synthetic image data, to segment the cell volumes with highly heterogeneous fluorescence intensity levels and to detect individual filopodial protrusions, followed by a constrained nearest-neighbor tracking algorithm to obtain valuable information about the spatio-temporal evolution of individual filopodia. We validated the workflow using real and synthetic 3-D time-lapse sequences of lung adenocarcinoma cells of three morphologically distinct filopodial phenotypes and show that it achieves reliable segmentation and tracking performance, providing a robust, reproducible and less time-consuming alternative to manual analysis of the 3D+t image data.

Motivation: The functionality of neurons and their role in neuronal networks is tightly connected to the cell morphology. A fundamental problem in many neurobiological studies aiming to unravel this connection is the digital reconstruction of neuronal cell morphology from microscopic image data. Many methods have been developed for this, but they are far from perfect, and better methods are needed. Results: Here we present a new method for tracing neuron centerlines needed for full reconstruction. The method uses a fundamentally different approach than previous methods by considering neuron tracing as a Bayesian multi-object tracking problem. The problem is solved using probability hypothesis density filtering. Results of experiments on 2D and 3D fluorescence microscopy image datasets of real neurons indicate the proposed method performs comparably or even better than the state of the art. Availability and Implementation: Software implementing the proposed neuron tracing method was written in the Java programming language as a plugin for the ImageJ platform. Source code is freely available for non-commercial use at https://bitbucket.org/miroslavradojevic/phd . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

The distributed resampling algorithm with non-proportional allocation (RNA) [1] is key to implementing particle filtering applications on parallel computer systems. We extend the original work by Bolić et al. by introducing an adaptive RNA (ARNA) algorithm, improving RNA by dynamically adjusting the particle-exchange ratio and randomizing the process ring topology. This improves the runtime performance of ARNA by about 9% over RNA with 10% particle exchange. ARNA also significantly improves the speed at which information is shared between processing elements, leading to about 20-fold faster convergence. The ARNA algorithm requires only a few modifications to the original RNA, and is hence easy to implement.

MOTIVATION: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. RESULTS: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. AVAILABILITY AND IMPLEMENTATION: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.

MOTIVATION: Complete, accurate and reproducible analysis of intracellular foci from fluorescence microscopy image sequences of live cells requires full automation of all processing steps involved: cell segmentation and tracking followed by foci segmentation and pattern analysis. Integrated systems for this purpose are lacking. RESULTS: Extending our previous work in cell segmentation and tracking, we developed a new system for performing fully automated analysis of fluorescent foci in single cells. The system was validated by applying it to two common tasks: intracellular foci counting (in DNA damage repair experiments) and cell-phase identification based on foci pattern analysis (in DNA replication experiments). Experimental results show that the system performs comparably to expert human observers. Thus, it may replace tedious manual analyses for the considered tasks, and enables high-content screening. AVAILABILITY AND IMPLEMENTATION: The described system was implemented in MATLAB (The MathWorks, Inc., USA) and compiled to run within the MATLAB environment. The routines together with four sample datasets are available at http://celmia.bigr.nl/. The software is planned for public release, free of charge for non-commercial use, after publication of this article.

Studying intracellular dynamics is of fundamental importance for understanding healthy life at the molecular level and for developing drugs to target disease processes. One of the key technologies to enable this research is the automated tracking and motion analysis of these objects in microscopy image sequences. To make better use of the spatiotemporal information than common frame-by-frame tracking methods, two alternative approaches have recently been proposed, based upon either Bayesian estimation or space-time segmentation. In this paper, we propose to combine the power of both approaches, and develop a new probabilistic method to segment the traces of the moving objects in kymograph representations of the image data. It is based on variable-rate particle filtering and uses multiscale trend analysis of the extracted traces to estimate the relevant kinematic parameters. Experiments on realistic synthetically generated images as well as on real biological image data demonstrate the improved potential of the new method for the analysis of microtubule dynamics in vitro.

Cell segmentation and tracking in time-lapse fluorescence microscopy images is a task of fundamental importance in many biological studies on cell migration and proliferation. In recent years, level sets have been shown to provide a very appropriate framework for this purpose, as they are well suited to capture topological changes occurring during mitosis, and they easily extend to higher dimensional image data. This model evolution approach has also been extended to deal with many cells concurrently. Notwithstanding its high potential, the multiple-level-set method suffers from a number of shortcomings, which limit its applicability to a larger variety of cell biological imaging studies. In this paper, we propose several modifications and extensions to the coupled-active-surfaces algorithm, which considerably improve its robustness and applicability. Our algorithm was validated by comparing it to the original algorithm and two other cell segmentation algorithms. For the evaluation, four real fluorescence microscopy image datasets were used, involving different cell types and labelings that are representative of a large range of biological experiments. Improved tracking performance in terms of precision (up to 11%), recall (up to 8%), ability to correctly capture all cell division events, and computation time (up to nine times reduction) is achieved.

In the above titled paper (ibid., vol. 29, no. 3, pp. 852-867, Mar. 10), several figure citations were incorrect. The correct figure citations are provided here.

Quantitative analysis of biological image data generally involves the detection of many subresolution spots. Especially in live cell imaging, for which fluorescence microscopy is often used, the signal-to-noise ratio (SNR) can be extremely low, making automated spot detection a very challenging task. In the past, many methods have been proposed to perform this task, but a thorough quantitative evaluation and comparison of these methods is lacking in the literature. In this paper, we evaluate the performance of the most frequently used detection methods for this purpose. These include seven unsupervised and two supervised methods. We perform experiments on synthetic images of three different types, for which the ground truth was available, as well as on real image data sets acquired for two different biological studies, for which we obtained expert manual annotations to compare with. The results from both types of experiments suggest that for very low SNRs ( approximately 2), the supervised (machine learning) methods perform best overall. Of the unsupervised methods, the detectors based on the so-called h -dome transform from mathematical morphology or the multiscale variance-stabilizing transform perform comparably, and have the advantage that they do not require a cumbersome learning stage. At high SNRs ( > 5), the difference in performance of all considered detectors becomes negligible.

Quantitative analysis of dynamic processes in living cells by means of fluorescence microscopy imaging requires tracking of hundreds of bright spots in noisy image sequences. Deterministic approaches, which use object detection prior to tracking, perform poorly in the case of noisy image data. We propose an improved, completely automatic tracker, built within a Bayesian probabilistic framework. It better exploits spatiotemporal information and prior knowledge than common approaches, yielding more robust tracking also in cases of photobleaching and object interaction. The tracking method was evaluated using simulated but realistic image sequences, for which ground truth was available. The results of these experiments show that the method is more accurate and robust than popular tracking methods. In addition, validation experiments were conducted with real fluorescence microscopy image data acquired for microtubule growth analysis. These demonstrate that the method yields results that are in good agreement with manual tracking performed by expert cell biologists. Our findings suggest that the method may replace laborious manual procedures.

We establish a link between classical osculatory interpolation and modern convolution-based interpolation and use it to show that two well-known cubic convolution schemes are formally equivalent to two osculatory interpolation schemes proposed in the actuarial literature about a century ago. We also discuss computational differences and give examples of other cubic interpolation schemes not previously studied in signal and image processing.

This paper presents a chronological overview of the developments in interpolation theory, from the earliest times to the present date. It brings out the connections between the results obtained in different ages, thereby putting the techniques currently used in signal and image processing into historical perspective. A summary of the insights and recommendations that follow from relatively recent theoretical as well as experimental studies concludes the presentation.

In digital subtraction angiography (DSA), patient motion is the primary cause of image quality degradation. The motion correction algorithms developed so far were not sufficiently fast so as to be suitable for integration in a clinical setting. In this paper we describe a new image registration technique for motion artifact reduction in DSA which is fully automatic, effective, and computationally very efficient. Using an image content driven control point selection mechanism and modern graphics hardware for image warping, the algorithm requires less than one second per DSA image (on average). Preliminary experiments on cerebral DSA images illustrate the applicability of the technique.

A classical approach to interpolation of sampled data is polynomial interpolation. However, from the sampling theorem it follows that the ideal approach to interpolation is to convolve the given samples with the sinc function. In this paper we study the properties of the sinc-approximating kernels that can be derived from the Lagrange central interpolation scheme. Both the finite-extent properties and the convergence property are analyzed. The Lagrange central interpolation kernels of up to ninth order are compared to cardinal splines of corresponding orders, both by spectral analysis and by rotation experiments on real-life test-images. It is concluded that cardinal spline interpolation is by far superior.

The reconstruction of images is an important operation in many applications. From sampling theory, it is well known that the sine-function is the ideal interpolation kernel which, however, cannot be used in practice. In order to be able to obtain an acceptable reconstruction, both in terms of computational speed and mathematical precision, it is required to design a kernel that is of finite extent and resembles the sinc-function as much as possible. In this paper, the applicability of the sine-approximating symmetrical piecewise nth-order polynomial kernels is investigated in satisfying these requirements. After the presentation of the general concept, kernels of first, third, fifth and seventh order are derived. An objective, quantitative evaluation of the reconstruction capabilities of these kernels is obtained by analyzing the spatial and spectral behavior using different measures, and by using them to translate, rotate, and magnify a number of real-life test images. From the experiments, it is concluded that while the improvement of cubic convolution over linear interpolation is significant, the use of higher order polynomials only yields marginal improvement.

Digital subtraction angiography (DSA) is a well-established modality for the visualization of blood vessels in the human body. A serious disadvantage of this technique, inherent to the subtraction operation, is its sensitivity to patient motion. The resulting artifacts frequently reduce the diagnostic value of the images. Over the past two decades, many solutions to this problem have been put forward. In this paper, we give an overview of the possible types of motion artifacts and the techniques that have been proposed to avoid them. The main purpose of this paper is to provide a detailed review and discussion of retrospective motion correction techniques that have been described in the literature, to summarize the conclusions that can be drawn from these studies, and to provide suggestions for future research.