Scribble supervision reduces annotation costs in multi-organ segmentation. However, its sparsity results in insufficient supervision for most regions and inadequate feature learning in hard areas (e.g., organ boundaries). These hard areas cause model confirmation bias and high epistemic uncertainty, which existing methods fail to address. To overcome these core challenges, we propose an epistemic-driven hardness-adaptive focusing framework. This framework establishes a self-improving loop: quantified epistemic uncertainty guides hard sample generation, while hard sample learning and feature alignment jointly reduce epistemic uncertainty. Specifically, we first propose a phase-adaptive hardness-aware loss function to quantify epistemic uncertainty and generate dynamic hardness maps during training. Based on these maps, we employ a distribution-divergence-aware copy-paste operation to create hard samples, which are progressively incorporated into learning to reduce epistemic uncertainty. Furthermore, we introduce feature distribution alignment to mitigate bias and epistemic uncertainty by aligning organ-specific hard regions with global features. Extensive experiments on multi-organ CT and ultrasound datasets demonstrate the competitiveness and effectiveness of our method. The framework's generalizability and robustness are further validated under cross-dataset and noise-corrupted scenarios. This work offers a practical solution for clinical applications where annotation efficiency is critical.

Semantic segmentation of ultrasound (US) images with deep learning has played a crucial role in computer-aided disease screening, diagnosis and prognosis. However, due to the scarcity of US images and small field of view, resulting segmentation models are tailored for a specific single organ and may lack robustness, overlooking correlations among anatomical structures of multiple organs. To address these challenges, we propose the Multi-Organ FOundation (MOFO) model for universal US image segmentation. The MOFO is optimized jointly from multiple organs across various anatomical regions to overcome the data scarcity and explore correlations between multiple organs. The MOFO extracts organ-invariant representations from US images. Simultaneously, the task prompt is employed to refine organ-specific representations for segmentation predictions. Moreover, the anatomical prior is incorporated to enhance the consistency of the anatomical structures. A multi-organ US database with segmentation labels, comprising 7039 images from 10 organs across various regions of the human body, has been established to develop and evaluate our model. Results demonstrate that the MOFO outperforms single-organ methods in terms of the Dice coefficient, 95% Hausdorff distance and average symmetric surface distance with statistically sufficient margins. Our experiments in multi-organ universal segmentation for US images serve as a pioneering exploration of improving segmentation performance by leveraging semantic and anatomical relationships within US images of multiple organs.

Segmenting internal structure from echocardiography is essential for the diagnosis and treatment of various heart diseases. Semi-supervised learning shows its ability in alleviating annotations scarcity. While existing semi-supervised methods have been successful in image segmentation across various medical imaging modalities, few have attempted to design methods specifically addressing the challenges posed by the poor contrast, blurred edge details and noise of echocardiography. These characteristics pose challenges to the generation of high-quality pseudo-labels in semi-supervised segmentation based on Mean Teacher. Inspired by human reflection on erroneous practices, we devise an error reflection strategy for echocardiography semi-supervised segmentation architecture. The process triggers the model to reflect on inaccuracies in unlabeled image segmentation, thereby enhancing the robustness of pseudo-label generation. Specifically, the strategy is divided into two steps. The first step is called reconstruction reflection. The network is tasked with reconstructing authentic proxy images from the semantic masks of unlabeled images and their auxiliary sketches, while maximizing the structural similarity between the original inputs and the proxies. The second step is called guidance correction. Reconstruction error maps decouple unreliable segmentation regions. Then, reliable data that are more likely to occur near high-density areas are leveraged to guide the optimization of unreliable data potentially located around decision boundaries. Additionally, we introduce an effective data augmentation strategy, termed as multi-scale mixing up strategy, to minimize the empirical distribution gap between labeled and unlabeled images and perceive diverse scales of cardiac anatomical structures. Extensive experiments on a public echocardiography dataset CAMUS, and a private clinical echocardiography dataset demonstrate the competitiveness of the proposed method.

The accurate segmentation of prostate region in magnetic resonance imaging (MRI) can provide reliable basis for artificially intelligent diagnosis of prostate cancer. Transformer-based models have been increasingly used in image analysis due to their ability to acquire long-term global contextual features. Although Transformer can provide feature representations of the overall appearance and contour representations at long distance, it does not perform well on small-scale datasets of prostate MRI due to its insensitivity to local variation such as the heterogeneity of the grayscale intensities in the peripheral zone and transition zone across patients; meanwhile, the convolutional neural network (CNN) could retain these local features well. Therefore, a robust prostate segmentation model that can aggregate the characteristics of CNN and Transformer is desired. In this work, a U-shaped network based on the convolution coupled Transformer is proposed for segmentation of peripheral and transition zones in prostate MRI, named the convolution coupled Transformer U-Net (CCT-Unet). The convolutional embedding block is first designed for encoding high-resolution input to retain the edge detail of the image. Then the convolution coupled Transformer block is proposed to enhance the ability of local feature extraction and capture long-term correlation that encompass anatomical information. The feature conversion module is also proposed to alleviate the semantic gap in the process of jumping connection. Extensive experiments have been conducted to compare our CCT-Unet with several state-of-the-art methods on both the ProstateX open dataset and the self-bulit Huashan dataset, and the results have consistently shown the accuracy and robustness of our CCT-Unet in MRI prostate segmentation.

Spatial resolution is a critical imaging parameter in magnetic resonance imaging. The image super-resolution (SR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images. Over the past several years, the convolutional neural networks (CNN)-based SR methods have achieved state-of-the-art performance. However, CNNs with very deep network structures usually suffer from the problems of degradation and diminishing feature reuse, which add difficulty to network training and degenerate the transmission capability of details for SR. To address these problems, in this work, a progressive wide residual network with a fixed skip connection (named FSCWRN) based SR algorithm is proposed to reconstruct MR images, which combines the global residual learning and the shallow network based local residual learning. The strategy of progressive wide networks is adopted to replace deeper networks, which can partially relax the above-mentioned problems, while a fixed skip connection helps provide rich local details at high frequencies from a fixed shallow layer network to subsequent networks. The experimental results on one simulated MR image database and three real MR image databases show the effectiveness of the proposed FSCWRN SR algorithm, which achieves improved reconstruction performance compared with other algorithms.

The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), has shown its effectiveness for AD diagnosis. The deep polynomial networks (DPN) is a recently proposed deep learning algorithm, which performs well on both large-scale and small-size datasets. In this study, a multimodal stacked DPN (MM-SDPN) algorithm, which MM-SDPN consists of two-stage SDPNs, is proposed to fuse and learn feature representation from multimodal neuroimaging data for AD diagnosis. Specifically speaking, two SDPNs are first used to learn high-level features of MRI and PET, respectively, which are then fed to another SDPN to fuse multimodal neuroimaging information. The proposed MM-SDPN algorithm is applied to the ADNI dataset to conduct both binary classification and multiclass classification tasks. Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.

The computer-aided diagnosis for histopathological images has attracted considerable attention. Principal component analysis network (PCANet) is a novel deep learning algorithm for feature learning with the simple network architecture and parameters. In this study, a color pattern random binary hashing-based PCANet (C-RBH-PCANet) algorithm is proposed to learn an effective feature representation from color histopathological images. The color norm pattern and angular pattern are extracted from the principal component images of R, G, and B color channels after cascaded PCA networks. The random binary encoding is then performed on both color norm pattern images and angular pattern images to generate multiple binary images. Moreover, we rearrange the pooled local histogram features by spatial pyramid pooling to a matrix-form for reducing the dimension of feature and preserving spatial information. Therefore, a C-RBH-PCANet and matrix-form classifier-based feature learning and classification framework is proposed for diagnosis of color histopathological images. The experimental results on three color histopathological image datasets show that the proposed C-RBH-PCANet algorithm is superior to the original PCANet and other conventional unsupervised deep learning algorithms, while the best performance is achieved by the proposed feature learning and classification framework that combines C-RBH-PCANet and matrix-form classifier.

Endothelial permeability is associated with the genesis and development of atherosclerosis. Computerized image analysis is utilized to investigate the relationship between endothelial permeability and endothelial morphology. First, microscopic images are segmented to detect endothelial cells using the speckle reduction anisotropic diffusion and marker-controlled watershed, whose optimal parameter settings are obtained from the cell detection receiver operating characteristic. Two categories of morphological features are then extracted, including cell shape features and intercellular features. Finally, Student's t-test is conducted to explore the relation of the morphology to permeability. The method correctly detected 82.3% cells in two test images, while the over-segmented and fused cells were 8.4% and 9.3%, respectively. T-tests using images from two porcine coronary arteries demonstrated that four features had significant difference (P<0.05) between regions with highest (top 25%) and lowest (bottom 25%) albumin permeability. This finding is helpful in exploring the mechanisms responsible for high permeability.