The thickness of the diaphragm serves as a crucial biometric indicator, particularly in assessing rehabilitation and respiratory dysfunction. However, measuring diaphragm thickness from ultrasound images mainly depends on manual delineation of the fascia, which is subjective, time-consuming, and sensitive to the inherent speckle noise. In this study, we introduce an edge-aware diffusion segmentation model (ESADiff), which incorporates prior structural knowledge of the fascia to improve the accuracy and reliability of diaphragm thickness measurements in ultrasound imaging. We first apply a diffusion model, guided by annotations, to learn the image features while preserving edge details through an iterative denoising process. Specifically, we design an anisotropic edge-sensitive annotation refinement module that corrects inaccurate labels by integrating Hessian geometric priors with a backtracking shortest-path connection algorithm, further enhancing model accuracy. Moreover, a curvature-aware deformable convolution and edge-prior ranking loss function are proposed to leverage the shape prior knowledge of the fascia, allowing the model to selectively focus on relevant linear structures while mitigating the influence of noise on feature extraction. We evaluated the proposed model on an in-house diaphragm ultrasound dataset, a public calf muscle dataset, and an internal tongue muscle dataset to demonstrate robust generalization. Extensive experimental results demonstrate that our method achieves finer fascia segmentation and significantly improves the accuracy of thickness measurements compared to other state-of-the-art techniques, highlighting its potential for clinical applications.

Surgical workflow recognition (SWR) stands as a pivotal component in computer-assisted surgery and is dedicated to identifying phases from surgical videos. Many deep learning-based methods have been proposed for this task and achieved acceptable SWR results. However, these methods usually implicitly extract and aggregate spatio-temporal features, so that it is challenging for these methods to adequately use some spatial information that is strongly relevant to surgical phase in SWR task, such as the information from the surgical instruments. To address this issue, an Explicit Surgical Instrument Prompting (ESIP) approach is proposed for SWR task. ESIP leverages surgical instrument segmentation to generate instrument-specific visual prompts, which explicitly guide the extraction of crucial intra-frame spatial features through a frozen pre-trained backbone, then enable effective inter-frame spatio-temporal feature extraction and aggregation. Unlike multi-task approaches that jointly perform SWR with auxiliary tasks within a shared network framework, ESIP is a single-task SWR approach dedicated to optimize framework itself for more adequate feature extraction. Furthermore, to accomplish the segmentation prompting efficiently, this paper presents SAM-based segmentation with prompt tuning strategy to explicitly integrate segmentation features into spatial features. Experimental results on Cholec80, M2CAI and AutoLaparo datasets demonstrate that our ESIP method achieves the best performance in comparison with 16 SOTA methods, with a Precision of 91.8%, 89.5% and 89.6%, Recall of 92.2%, 89.5% and 76.9%, Jaccard of 83.3%, 77.0% and 67.3%, respectively.

Accurate detection of surgical instruments is critical for both routine surgical procedures and surgical robotics research. To the best of our knowledge, there is a notable lack of datasets and dedicated detection studies specifically addressing orthopedic surgical instruments. Detecting orthopedic surgical instruments presents particular challenges including significant size variations, highly similar shapes, and frequent, severe occlusions due to instrument intersections. To address these issues, we propose an orthopedic surgical instrument detection method (OrthoDetNet) incorporating three specialized modules. The FilterUnit mitigates occlusion effects via an adaptive feature filtering mechanism, that dynamically adjusts its filtering strategy based on context, prioritizing features from key regions while suppressing distracting interference features. The DEUnit enhances fine-grained feature discrimination in local regions to distinguish instruments with high shape similarity, and the BDFusion module improves multi-scale detection performance through bi-directional feature fusion between deep and shallow-level feature maps. A dataset for orthopedic surgical instrument detection is created, which is based on the proximal femoral nail antirotation (PFNA) instrument package manufactured by Shenzhen Mindray Bio-Medical Electronics Co., Ltd. Images were captured in a controlled, simulated experimental environment, ensuring no patient privacy or ethical concerns. We obtained explicit authorization from the manufacturer for instrument use. Experimental results on this dataset demonstrate the effectiveness of the OrthoDetNet and its constituent modules.

Ultrasound image segmentation is critical for tumor screening but is hindered by noise, artifacts, and high variability in lesion appearance. Challenges like blurred boundaries and morphological similarities further complicate accurate delineation. To address this, we propose the Dynamic Edge-aware Transformer with Cross-image Patch Dependency (DET-CPD). Our model integrates two key modules: a Dynamic Difference Convolution Module (DDCM) to enhance edge representation for varied lesions, and a Cross-Scale Semantic Enhancement Module (CSEM) that leverages cross-scale channel information to distinguish tumors from surrounding tissue. Crucially, we introduce a novel Cross-image Patch Dependency Loss (CPDLoss) that captures semantic dependencies across different images in a batch, improving robustness. Extensive experiments on four public datasets (BUSI, DatasetB, DDTI, and TN3K) demonstrate that DET-CPD achieves state-of-the-art segmentation performance.

Accurate automated measurement of diaphragmatic thickness in ultrasound imaging is a critical challenging task for respiratory function assessment, primarily due to difficulties in precise fascial identification. And ultrasound visualization of the diaphragm is characterized by unique challenges, including discontinuous and blurred boundary delineations caused by imaging artifacts, as well as interference and influence from adjacent muscular reverberations. These problems are further compounded by subjects’ pose variations during image acquisition. To address these challenges, we introduce IBS-Net, an innovative triple-branch interactive segmentation network that synergistically combines boundary regression with auxiliary task learning to optimize feature representation in segmentation task. Moreover, Our framework incorporates two innovative module: an Adaptive Fusion Module (AFM) that enables multi-scale hierarchical feature refinement for precise boundary characterization, and a Cross Interactive Module (CIM) that employs parallel-encoded feature extraction to simultaneously achieve accurate fascial localization while preserving structural topology. These complementary mechanisms effectively resolve spatial feature inconsistencies, facilitating robust multi-level feature integration. Comprehensive experimental results demonstrate that IBS-Net achieves statistically significant improvements of 8.9% in Dice similarity coefficient and 8.05% in Jaccard index compared to conventional methods. Moreover, to verify the effectiveness of the proposed method, we extended it to other publicly available BUSI dataset for experimentation. The results demonstrate that our method is competitive in terms of both accuracy and completeness in the identification of fuzzy boundaries in ultrasound images.

One of the core challenges in ultrasound image quality assessment (IQA) is the entanglement of semantic content and quality-related information, such as blurring and shadows. Insufficient attention to the latter can easily lead to biased IQA results. Furthermore, fine-grained quality inconsistencies, i.e., subtle variations in ultrasound images that can impact quality interpretations, may further complicate the IQA tasks. To address these challenges, we propose a novel degradation-aware model (DAM) for the ultrasound IQA, which effectively perceives various and subtle variations of quality patterns, accurately assessing the quality of ultrasound images. The advanced degradation-derived augmentation (DDA) in DAM incorporates degradations that clinicians may focus on during IQA into the synthesis of appearance changes, promoting the disentanglement of quality-related representations from semantic contents. Subsequently, we present fine-grained degradation learning (FGDL), which encourages distinctions between image versions with diminishing quality inconsistencies, boosting the awareness of quality nuances from easy to hard for better ultrasound IQA performance. A universal boundary acquisition operator (UBAO) is also developed to suppress interferences from redundant information, achieving the standardization of ultrasound images from various devices. Extensive experimental results on an in-house ultrasound dataset demonstrate that DAM outperforms 14 baseline methods, achieving a PLCC of 0.760 and an SROCC of 0.766. The code can be available at this URL.

Ultrasound imaging has emerged as an effective tool for aiding diagnosis. The automatic segmentation of ultrasound images is crucial in identifying the lesion target and evaluating clinical indicators for accurate diagnosis and prognosis. However, the segmentation problems are challenging due to the inherent speckle noise interference and low contrast of ultrasound images. The complex-value-based neural network can directly deal with the phase components, offering a potential solution in a better-perceiving structure for ultrasound image segmentation. In this study, we develop a Frequency Phase-Guided Attention Network (FPGANet) for ultrasound image segmentation by exploring the properties of the complex-valued model under the guide of phase and frequency perspectives. First, our proposed method transforms images into a complex domain as the input to an advanced complex-value model consisting of pure complex-value convolutions and operations. Especially this model can then effectively scrutinize phase information to distinguish target areas from similar backgrounds better. Moreover, we introduce a complex hybrid attention module following complex convolution to selectively adjust the perception of phase components and the model's bias. Also, we designed a frequency-adaptive separation module to emphasize frequency features prioritized by the encoder and decoder using a combination of wavelet decomposition and frequency channel attention. We evaluate the proposed FPGANet on three publicly available ultrasound datasets of breast, cardiac and thyroid nodules and a private abdominal effusion ultrasound dataset. Comparative experiments were also conducted with state-of-the-art methods. The results demonstrate the superior performance of FPGANet, implying its potential for advancing ultrasound image segmentation.

Deciphering coordinated movements is integral to understanding the daily activities and interactions between the nervous system and muscles, especially in robot-assisted rehabilitation. This study proposes a novel temporal-guided adaptive graph learning (TAGL) network to recognize coordinated movements from functional near-infrared spectroscopy (fNIRS) data. The temporal-guided node construction module is designed to build graph nodes while considering spatiotemporal and causal dependencies. Given the brain network's affinity for learning asymmetric structures, an adaptive edge learning module is devised, integrating a multihead attention mechanism for the tailored acquisition of directional edge connections among nodes. The TAGL model undergoes evaluation on both a proprietary fNIRS dataset featuring eight circular finger movements and a public fNIRS dataset involving three distinct actions. Comparative experiments with state-of-the-art methods reveal its superior performance, showcasing its potential in deciphering coordinated movements effectively.

Automated segmentation of liver tumors in CT scans is pivotal for diagnosing and treating liver cancer, offering a valuable alternative to labor-intensive manual processes and ensuring the provision of accurate and reliable clinical assessment. However, the inherent variability of liver tumors, coupled with the challenges posed by blurred boundaries in imaging characteristics, presents a substantial obstacle to achieving their precise segmentation. In this paper, we propose a novel dual-branch liver tumor segmentation model, SBCNet, to address these challenges effectively. Specifically, our proposed method introduces a contextual encoding module, which enables a better identification of tumor variability using an advanced multi-scale adaptive kernel. Moreover, a boundary enhancement module is designed for the counterpart branch to enhance the perception of boundaries by incorporating contour learning with the Sobel operator. Finally, we propose a hybrid multi-task loss function, concurrently concerning tumors' scale and boundary features, to foster interaction across different tasks of dual branches, further improving tumor segmentation. Experimental validation on the publicly available LiTS dataset demonstrates the practical efficacy of each module, with SBCNet yielding competitive results compared to other state-of-the-art methods for liver tumor segmentation.

Image segmentation achieves significant improvements with deep neural networks at the premise of a large scale of labeled training data, which is laborious to assure in medical image tasks. Recently, semi-supervised learning (SSL) has shown great potential in medical image segmentation. However, the influence of the learning target quality for unlabeled data is usually neglected in these SSL methods. Therefore, this study proposes a novel self-correcting co-training scheme to learn a better target that is more similar to ground-truth labels from collaborative network outputs. Our work has three-fold highlights. First, we advance the learning target generation as a learning task, improving the learning confidence for unannotated data with a self-correcting module. Second, we impose a structure constraint to encourage the shape similarity further between the improved learning target and the collaborative network outputs. Finally, we propose an innovative pixel-wise contrastive learning loss to boost the representation capacity under the guidance of an improved learning target, thus exploring unlabeled data more efficiently with the awareness of semantic context. We have extensively evaluated our method with the state-of-the-art semi-supervised approaches on four public-available datasets, including the ACDC dataset, M&Ms dataset, Pancreas-CT dataset, and Task_07 CT dataset. The experimental results with different labeled-data ratios show our proposed method's superiority over other existing methods, demonstrating its effectiveness in semi-supervised medical image segmentation.

The automatic and dependable identification of colonic disease subtypes by colonoscopy is crucial. Once successful, it will facilitate clinically more in-depth disease staging analysis and the formulation of more tailored treatment plans. However, inter-class confusion and brightness imbalance are major obstacles to colon disease subtyping. Notably, the Fourier-based image spectrum, with its distinctive frequency features and brightness insensitivity, offers a potential solution. To effectively leverage its advantages to address the existing challenges, this article proposes a framework capable of thorough learning in the frequency domain based on four core designs: the position consistency module, the high-frequency self-supervised module, the complex number arithmetic model, and the feature anti-aliasing module. The position consistency module enables the generation of spectra that preserve local and positional information while compressing the spectral data range to improve training stability. Through band masking and supervision, the high-frequency autoencoder module guides the network to learn useful frequency features selectively. The proposed complex number arithmetic model allows direct spectral training while avoiding the loss of phase information caused by current general-purpose real-valued operations. The feature anti-aliasing module embeds filters in the model to prevent spectral aliasing caused by down-sampling and improve performance. Experiments are performed on the collected five-class dataset, which contains 4591 colorectal endoscopic images. The outcomes show that our proposed method produces state-of-the-art results with an accuracy rate of 89.82%.

Echocardiography is an essential examination for cardiac disease diagnosis, from which anatomical structures segmentation is the key to assessing various cardiac functions. However, the obscure boundaries and large shape deformations due to cardiac motion make it challenging to accurately identify the anatomical structures in echocardiography, especially for automatic segmentation. In this study, we propose a dual-branch shape-aware network (DSANet) to segment the left ventricle, left atrium, and myocardium from the echocardiography. Specifically, the elaborate dual-branch architecture integrating shape-aware modules boosts the corresponding feature representation and segmentation performance, which guides the model to explore shape priors and anatomical dependence using an anisotropic strip attention mechanism and cross-branch skip connections. Moreover, we develop a boundary-aware rectification module together with a boundary loss to regulate boundary consistency, adaptively rectifying the estimation errors nearby the ambiguous pixels. We evaluate our proposed method on the publicly available and in-house echocardiography dataset. Comparative experiments with other state-of-the-art methods demonstrate the superiority of DSANet, which suggests its potential in advancing echocardiography segmentation.

Hard sample selection can effectively improve model convergence by extracting the most representative samples from a training set. However, due to the large capacity of medical images, existing sampling strategies suffer from insufficient exploitation for hard samples or high time cost for sample selection when adopted by 3D patch-based models in the field of multi-organ segmentation. In this paper, we present a novel and effective online hard patch mining (OHPM) algorithm. In our method, an average shape model that can be mapped with all training images is constructed to guide the exploration of hard patches and aggregate feedback from predicted patches. The process of hard mining is formalized as a multi-armed bandit problem and solved with bandit algorithms. With the shape model, OHPM requires negligible time consumption and can intuitively locate difficult anatomical areas during training. The employment of bandit algorithms ensures online and sufficient hard mining. We integrate OHPM with advanced segmentation networks and evaluate them on two datasets containing different anatomical structures. Comparative experiments with other sampling strategies demonstrate the superiority of OHPM in boosting segmentation performance and improving model convergence. The results in each dataset with each network suggest that OHPM significantly outperforms other sampling strategies by nearly 2% average Dice score.

Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have improved results by directly encoding the appearance of the captured anatomy with the likelihood maps (i.e., heatmaps). However, most current solutions overlook another essence of heatmap regression, the objective metric for regressing target heatmaps and rely on hand-crafted heuristics to set the target precision, thus being usually cumbersome and task-specific. In this paper, we propose a novel learning-to-learn framework for landmark detection to optimize the neural network and the target precision simultaneously. The pivot of this work is to leverage the reinforcement learning (RL) framework to search objective metrics for regressing multiple heatmaps dynamically during the training process, thus avoiding setting problem-specific target precision. We also introduce an early-stop strategy for active termination of the RL agent's interaction that adapts the optimal precision for separate targets considering exploration-exploitation tradeoffs. This approach shows better stability in training and improved localization accuracy in inference. Extensive experimental results on two different applications of landmark localization: 1) our in-house prenatal ultrasound (US) dataset and 2) the publicly available dataset of cephalometric X-Ray landmark detection, demonstrate the effectiveness of our proposed method. Our proposed framework is general and shows the potential to improve the efficiency of anatomical landmark detection.

This paper presents an automated measurement of spine curvature by using prior knowledge on vertebral anatomical structures in ultrasound volume projection imaging (VPI). This method can be used in scoliosis assessment with free-hand 3-D ultrasound imaging. It is based on the extraction of bony features from VPI images using a newly proposed two-fold thresholding strategy, with information of the symmetric and asymmetric measures obtained from phase congruency. The spinous column profile is detected from the segmented bony regions, and it is further used to extract a curve representing spine profile. The spine curvature is then automatically calculated according to the inflection points along the curve. The algorithm was evaluated on volunteers with the different severity of scoliosis. The results obtained using the newly developed method had a good linear correlation with those by the manual method (r ≥ 0.90, p <; 0.001) and X-ray Cobb's method (r = 0.83, p <; 0.001). The bigger variations observed in the manual measurement also implied that the automatic method is more reliable. The proposed method can be a promising approach for facilitating the applications of 3-D ultrasound imaging in the diagnosis, treatment, and screening of scoliosis.

The standing radiograph is used as a gold standard to diagnose spinal deformity including scoliosis, a medical condition defined as lateral spine curvature > 10°. However, the health concern of X-ray and large inter-observer variation of measurements on X-ray images have significantly restricted its application, particularly for scoliosis screening and close follow-up for adolescent patients. In this study, a radiation-free freehand 3-D ultrasound system was developed for scoliosis assessment using a volume projection imaging method. Based on the obtained coronal view images, two measurement methods were proposed using transverse process and spinous profile as landmarks, respectively. As a reliability study, 36 subjects (age: 30.1 ±14.5; male: 12; female: 24) with different degrees of scoliosis were scanned using the system to test the inter- and intra-observer repeatability. The intra- and inter-observer tests indicated that the new assessment methods were repeatable, with ICC larger than 0.92. Small intra- and inter-observer variations of measuring spine curvature were observed for the two measurement methods (intra-: 1.4 ±1.0° and 1.4 ±1.1°; inter-: 2.2 ±1.6° and 2.5 ±1.6°). The results also showed that the spinal curvature obtained by the new method had good linear correlations with X-ray Cobb's method (R2 = 0.8, p < 0.001, 29 subjects). These results suggested that the ultrasound volume projection imaging method can be a promising approach for the assessment of scoliosis, and further research should be followed up to demonstrate its potential clinical applications for mass screening and curve progression and treatment outcome monitoring of scoliosis patients.

As a direct determinant parameter to quantify muscle activity, the muscle thickness (MT) has been investigated in many aspects and for various purposes. Ultrasonography (US) is a promising modality to detect muscle morphological changes during contractions since it is portable, noninvasive, and real time. However, there are few reports on sensitive and efficient estimation of changes of MT in a cross-sectional plane. In this feasibility investigation, we proposed a coarse-to-fine method based on a compressive-tracking algorithm for estimation of MT changes during an example task of isometric knee extension using ultrasound images. The sensitivity and efficiency are evaluated with 1920 US images from quadriceps muscle (QM) in eight subjects. The detection results were compared with those obtained from both traditional manual measurement and the well known normalized cross-correlation method, and the effect of the size of tracking window on detection performance was evaluated as well. It is demonstrated that the proposed method agrees well with the manual measurement. Meanwhile, it is not only sensitive to relatively small changes of MT but also computationally efficient.