Breast cancer poses a major health concern for women globally. Effective segmentation of breast tumors for ultrasound images is crucial for early diagnosis and treatment. Conventional convolutional neural networks have shown promising results in this domain but face challenges due to image complexities and are computationally expensive, limiting their practical application in real-time clinical settings. We propose Eff-AResUNet-GL, a segmentation model using EfficienetNet-B3 as the encoder. This design integrates attention gates in skip connections to focus on significant features and residual blocks in the decoder to retain details and reduce gradient loss. Additionally, guided loss functions are applied at each decoder layer to generate better features, subsequently improving segmentation accuracy. Experimental results on BUSIS and Dataset B demonstrate that Eff-AResUNet-GL achieves superior performance and computational efficiency compared to state-of-the-art models, showing robustness in handling complex breast ultrasound images. Eff-AResUNet-GL offers a practical, high-performing solution for breast tumor segmentation, demonstrating potential clinical through improved segmentation accuracy and efficiency.

Medical image segmentation is crucial for computer-aided diagnosis and treatment planning, directly influencing clinical decision-making. To enhance segmentation accuracy, existing methods typically fuse local, global, and various other features. However, these methods often ignore the negative impact of noise on the results during the feature fusion process. In contrast, certain regions of the human visual system, such as the inferotemporal cortex and parietal cortex, effectively suppress irrelevant noise while integrating multiple features-a capability lacking in current methods. To address this gap, we propose MS-Net, a medical image segmentation network inspired by human visual perception. MS-Net incorporates a multi-feature compression (MFC) module that mimics the human visual system's processing of complex images, first learning various feature types and subsequently filtering out irrelevant ones. Additionally, MS-Net features a segmentation refinement (SR) module that emulates how physicians segment lesions. This module initially performs coarse segmentation to capture the lesion's approximate location and shape, followed by a refinement step to achieve precise boundary delineation. Experimental results demonstrate that MS-Net not only attains state-of-the-art segmentation performance across three public datasets but also significantly reduces the number of parameters compared to existing models. Code is available at https://github.com/guangguangLi/MS-Net.

Emerging evidence suggests that diminished skeletal muscle mass is associated with lower health-related quality of life (HRQOL) in individuals with cancer. There are no studies that we know of in the literature that use ultrasound system to evaluate muscle mass and its relationship with HRQOL. The aim of our study was to evaluate the relationship between HRQOL determined by the EuroQol-5D tool and muscle mass determined by an artificial intelligence-based ultrasound system at the rectus femoris (RF) level in outpatients with cancer. Anthropometric data by bioimpedance (BIA), muscle mass by ultrasound by an artificial intelligence-based at the RF level, biochemistry determination, dynamometry and HRQOL were measured. A total of 158 patients with cancer were included with a mean age of 70.6 ±9.8 years. The mean body mass index was 24.4 ± 4.1 kg/m² with a mean body weight of 63.9 ± 11.7 kg (38% females and 62% males). A total of 57 patients had a severe degree of malnutrition (36.1%). The distribution of the location of the tumors was 66 colon-rectum cancer (41.7%), 56 esophageal-stomach cancer (35.4%), 16 pancreatic cancer (10.1%), and 20.2% other locations. A positive correlation cross-sectional area (CSA), muscle thickness (MT), pennation angle, (BIA) parameters, and muscle strength was detected. Patients in the groups below the median for the visual scale and the EuroQol-5D index had lower CSA and MT, BIA, and muscle strength values. CSA (beta 4.25, 95% CI 2.03-6.47) remained in the multivariate model as dependent variable (visual scale) and muscle strength (beta 0.008, 95% CI 0.003-0.14) with EuroQol-5D index. Muscle strength and pennation angle by US were associated with better score in dimensions of mobility, self-care, and daily activities. CSA, MT, and pennation angle of RF determined by an artificial intelligence-based muscle ultrasound system in outpatients with cancer were related to HRQOL determined by EuroQol-5D.

Capturing rich multi-scale features is essential to address complex variations in medical image segmentation. Multiple hybrid networks have been developed to integrate the complementary benefits of convolutional neural networks (CNN) and Transformers. However, existing methods may suffer from either huge computational cost required by the complicated networks or unsatisfied performance of lighter networks. How to give full play to the advantages of both convolution and self-attention and design networks that are both effective and efficient still remains an unsolved problem. In this work, we propose a robust lightweight multi-stage hybrid architecture, named Multi-stage Aggregation Transformer version 2 (MA-TransformerV2), to extract multi-scale features with progressive aggregations for accurate segmentation of highly variable medical images at a low computational cost. Specifically, lightweight Trans blocks and lightweight CNN blocks are parallelly introduced into the dual-branch encoder module in each stage, and then a vector quantization block is incorporated at the bottleneck to discretizes the features and discard the redundance. This design not only enhances the representation capabilities and computational efficiency of the model, but also makes the model interpretable. Extensive experimental results on public datasets show that our method outperforms state-of-the-art methods, including CNN-based, Transformer-based, advanced hybrid CNN-Transformer-based models, and several lightweight models, in terms of both segmentation accuracy and model capacity. Code will be made publicly available at https://github.com/zjmiaprojects/MATransformerV2.

Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for tasks like cortical registration, parcellation, and thickness estimation. Traditionally, such analyses require high-resolution, isotropic scans with good gray-white matter contrast, typically a T1-weighted scan with 1 mm resolution. This requirement precludes application of these techniques to most MRI scans acquired for clinical purposes, since they are often anisotropic and lack the required T1-weighted contrast. To overcome this limitation and enable large-scale neuroimaging studies using vast amounts of existing clinical data, we introduce recon-all-clinical, a novel methodology for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and contrast. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions (SDFs), and classical geometry processing for accurate surface placement while maintaining topological and geometric constraints. The method does not require retraining for different acquisitions, thus simplifying the analysis of heterogeneous clinical datasets. We evaluated recon-all-clinical on multiple public datasets like ADNI, HCP, AIBL, OASIS and including a large clinical dataset of over 9,500 scans. The results indicate that our method produces geometrically precise cortical reconstructions across different MRI contrasts and resolutions, consistently achieving high accuracy in parcellation. Cortical thickness estimates are precise enough to capture aging effects, independently of MRI contrast, even though accuracy varies with slice thickness. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical, enabling researchers to perform detailed cortical analysis on the huge amounts of already existing clinical MRI scans. This advancement may be particularly valuable for studying rare diseases and underrepresented populations where research-grade MRI data is scarce.

Purpose To develop and evaluate an artificial intelligence-based model for real-time nuchal translucency (NT) plane identification and measurement in prenatal US assessments. Materials and Methods In this retrospective multicenter study conducted from January 2022 to October 2023, the Automated Identification and Measurement of NT (AIM-NT) model was developed and evaluated using internal and external datasets. NT plane assessment, including identification of the NT plane and measurement of NT thickness, was independently conducted by AIM-NT and experienced radiologists, with the results subsequently audited by radiology specialists and accuracy compared between groups. To assess alignment of artificial intelligence with radiologist workflow, discrepancies between the AIM-NT model and radiologists in NT plane identification time and thickness measurements were evaluated. Results The internal dataset included a total of 3959 NT images from 3153 fetuses, and the external dataset included 267 US videos from 267 fetuses. On the internal testing dataset, AIM-NT achieved an area under the receiver operating characteristic curve of 0.92 for NT plane identification. On the external testing dataset, there was no evidence of differences between AIM-NT and radiologists in NT plane identification accuracy (88.8% vs 87.6%, <i>P</i> = .69) or NT thickness measurements on standard and nonstandard NT planes (<i>P</i> = .29 and .59). AIM-NT demonstrated high consistency with radiologists in NT plane identification time, with 1-minute discrepancies observed in 77.9% of cases, and NT thickness measurements, with a mean difference of 0.03 mm and mean absolute error of 0.22 mm (95% CI: 0.19, 0.25). Conclusion AIM-NT demonstrated high accuracy in identifying the NT plane and measuring NT thickness on prenatal US images, showing minimal discrepancies with radiologist workflow. <b>Keywords:</b> Ultrasound, Fetus, Segmentation, Feature Detection, Diagnosis, Convolutional Neural Network (CNN) <i>Supplemental material is available for this article.</i> © RSNA, 2025 See also commentary by Horii in this issue.

Osteoarthritis is a degenerative condition that affects bones and cartilage, often leading to structural changes, including osteophyte formation, bone density loss, and the narrowing of joint spaces. Over time, this process may disrupt the glenohumeral (GH) joint functionality, requiring a targeted treatment. Various options are available to restore joint functions, ranging from conservative management to surgical interventions, depending on the severity of the condition. This work introduces an innovative deep learning framework to process shoulder CT scans. It features the semantic segmentation of the proximal humerus and scapula, the 3D reconstruction of bone surfaces, the identification of the GH joint region, and the staging of three common osteoarthritic-related conditions: osteophyte formation (OS), GH space reduction (JS), and humeroscapular alignment (HSA). Each condition was stratified into multiple severity stages, offering a comprehensive analysis of shoulder bone structure pathology. The pipeline comprised two cascaded CNN architectures: 3D CEL-UNet for segmentation and 3D Arthro-Net for threefold classification. A retrospective dataset of 571 CT scans featuring patients with various degrees of GH osteoarthritic-related pathologies was used to train, validate, and test the pipeline. Root mean squared error and Hausdorff distance median values for 3D reconstruction were 0.22 mm and 1.48 mm for the humerus and 0.24 mm and 1.48 mm for the scapula, outperforming state-of-the-art architectures and making it potentially suitable for a PSI-based shoulder arthroplasty preoperative plan context. The classification accuracy for OS, JS, and HSA consistently reached around 90% across all three categories. The computational time for the entire inference pipeline was less than 15 s, showcasing the framework's efficiency and compatibility with orthopedic radiology practice. The achieved reconstruction and classification accuracy, combined with the rapid processing time, represent a promising advancement towards the medical translation of artificial intelligence tools. This progress aims to streamline the preoperative planning pipeline, delivering high-quality bone surfaces and supporting surgeons in selecting the most suitable surgical approach according to the unique patient joint conditions.

Semi-supervised medical image segmentation is a class of machine learning paradigms for segmentation model training and inference using both labeled and unlabeled medical images, which can effectively reduce the data labeling workload. However, existing consistency semi-supervised segmentation models mainly focus on investigating more complex consistency strategies and lack efficient utilization of volumetric contextual information, which leads to vague or uncertain understanding of the boundary between the object and the background by the model, resulting in ambiguous or even erroneous boundary segmentation results. For this reason, this study proposes a hybrid uncertainty network CUAMT based on contextual information. In this model, a contextual information extraction module CIE is proposed, which learns the connection between image contexts by extracting semantic features at different scales, and guides the model to enhance learning contextual information. In addition, a hybrid uncertainty module HUM is proposed, which guides the model to focus on segmentation boundary information by combining the global and local uncertainty information of two different networks to improve the segmentation performance of the networks at the boundary. In the left atrial segmentation and brain tumor segmentation dataset, validation experiments were conducted on the proposed model. The experiments show that our model achieves 89.84%, 79.89%, and 8.73 on the Dice metric, Jaccard metric, and 95HD metric, respectively, which significantly outperforms several current SOTA semi-supervised methods. This study confirms that the CIE and HUM strategies are effective. A semi-supervised segmentation framework is proposed for medical image segmentation.

Brain lesion segmentation is challenging in medical image analysis, aiming to delineate lesion regions precisely. Deep learning (DL) techniques have recently demonstrated promising results across various computer vision tasks, including semantic segmentation, object detection, and image classification. This paper offers an overview of recent DL algorithms for brain tumor and stroke segmentation, drawing on literature from 2021 to 2024. It highlights the strengths, limitations, current research challenges, and unexplored areas in imaging-based brain lesion classification based on insights from over 250 recent review papers. Techniques addressing difficulties like class imbalance and multi-modalities are presented. Optimization methods for improving performance regarding computational and structural complexity and processing speed are discussed. These include lightweight neural networks, multilayer architectures, and computationally efficient, highly accurate network designs. The paper also reviews generic and latest frameworks of different brain lesion detection techniques and highlights publicly available benchmark datasets and their issues. Furthermore, open research areas, application prospects, and future directions for DL-based brain lesion classification are discussed. Future directions include integrating neural architecture search methods with domain knowledge, predicting patient survival levels, and learning to separate brain lesions using patient statistics. To ensure patient privacy, future research is anticipated to explore privacy-preserving learning frameworks. Overall, the presented suggestions serve as a guideline for researchers and system designers involved in brain lesion detection and stroke segmentation tasks.

U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray images that show pathologies, it often fails to include these critical pathological areas in the generated masks. To address this limitation, in our study, we tackled the challenge of precise segmentation and mask generation by developing a novel approach, using CycleGAN, that encompasses the areas affected by pathologies within the region of interest, allowing the extraction of relevant radiomic features linked to pathologies. Furthermore, we adopted a feature selection approach to focus the analysis on the most significant features. The results of our proposed pipeline are promising, with an average accuracy of 92.05% and an average AUC of 89.48% for the multi-label classification of effusion and infiltration acquired from the ChestX-ray14 dataset, using the XGBoost model. Furthermore, applying our methodology to the classification of the 14 diseases in the ChestX-ray14 dataset resulted in an average AUC of 83.12%, outperforming previous studies. This research highlights the importance of effective pathological mask generation and features selection for accurate classification of chest diseases. The promising results of our approach underscore its potential for broader applications in the classification of chest diseases.