Medical imaging has become an essential tool in the diagnosis and treatment of various diseases, and provides critical insights through ultrasound, MRI, and X-ray modalities. Despite its importance, challenges remain in the accurate segmentation and classification of complex structures owing to factors such as low contrast, noise, and irregular anatomical shapes. This study addresses these challenges by proposing a novel hybrid deep learning model that integrates the strengths of Convolutional Autoencoders (CAE), UNet, and SegNet architectures. In the preprocessing phase, a Convolutional Autoencoder is used to effectively reduce noise while preserving essential image details, ensuring that the images used for segmentation and classification are of high quality. The ability of CAE to denoise images while retaining critical features enhances the accuracy of the subsequent analysis. The developed model employs UNet for multiscale feature extraction and SegNet for precise boundary reconstruction, with Dynamic Feature Fusion integrated at each skip connection to dynamically weight and combine the feature maps from the encoder and decoder. This ensures that both global and local features are effectively captured, while emphasizing the critical regions for segmentation. To further enhance the model's performance, the Hybrid Emperor Penguin Optimizer (HEPO) was employed for feature selection, while the Hybrid Vision Transformer with Convolutional Embedding (HyViT-CE) was used for the classification task. This hybrid approach allows the model to maintain high accuracy across different medical imaging tasks. The model was evaluated using three major datasets: brain tumor MRI, breast ultrasound, and chest X-rays. The results demonstrate exceptional performance, achieving an accuracy of 99.92% for brain tumor segmentation, 99.67% for breast cancer detection, and 99.93% for chest X-ray classification. These outcomes highlight the ability of the model to deliver reliable and accurate diagnostics across various medical contexts, underscoring its potential as a valuable tool in clinical settings. The findings of this study will contribute to advancing deep learning applications in medical imaging, addressing existing research gaps, and offering a robust solution for improved patient care.

This article reviews advancements in fully automated abdominal CT interpretation over the past decade, with a focus on automated image analysis techniques such as quantitative analysis, computer-aided detection, and disease classification. For each abdominal organ, we review segmentation techniques, assess clinical applications and performance, and explore methods for detecting/classifying associated pathologies. We also highlight cutting-edge AI developments, including foundation models, large language models, and multimodal image analysis. While challenges remain in integrating AI into radiology practice, recent progress underscores its growing potential to streamline workflows, reduce radiologist burnout, and enhance patient care.

Manual annotation of medical images is a labor-intensive and time-consuming process, posing a significant bottleneck in the development and deployment of robust medical imaging AI systems. This paper introduces a novel Human-AI collaborative framework for medical image segmentation that substantially reduces the annotation burden by eliminating the need for explicit manual pixel-level labeling. The core innovation lies in a preference learning paradigm, where human experts provide minimal, intuitive feedback -- simply indicating whether an AI-generated segmentation is better or worse than a previous version. The framework comprises four key components: (1) an adaptable foundation model (FM) for feature extraction, (2) label propagation based on feature similarity, (3) a clicking agent that learns from human better-or-worse feedback to decide where to click and with which label, and (4) a multi-round segmentation learning procedure that trains a state-of-the-art segmentation network using pseudo-labels generated by the clicking agent and FM-based label propagation. Experiments on three public datasets demonstrate that the proposed approach achieves competitive segmentation performance using only binary preference feedback, without requiring experts to directly manually annotate the images.

Brain lesions are abnormalities or injuries in brain tissue that are often detectable using magnetic resonance imaging (MRI), which reveals structural changes in the affected areas. This broad definition of brain lesions includes areas of the brain that are irreversibly damaged, as well as areas of brain tissue that are deformed as a result of lesion growth or swelling. Despite the importance of differentiating between damaged and deformed tissue, existing lesion segmentation methods overlook this distinction, labeling both of them as a single anomaly. In this work, we introduce a diffusion model-based framework for analyzing and reversing the brain lesion process. Our pipeline first segments abnormal regions in the brain, then estimates and reverses tissue deformations by restoring displaced tissue to its original position, isolating the core lesion area representing the initial damage. Finally, we inpaint the core lesion area to arrive at an estimation of the pre-lesion healthy brain. This proposed framework reverses a forward lesion growth process model that is well-established in biomechanical studies that model brain lesions. Our results demonstrate improved accuracy in lesion segmentation, characterization, and brain labeling compared to traditional methods, offering a robust tool for clinical and research applications in brain lesion analysis. Since pre-lesion healthy versions of abnormal brains are not available in any public dataset for validation of the reverse process, we simulate a forward model to synthesize multiple lesioned brain images.

This paper presents a rapid and robust approach for 3D volumetric segmentation, labelling, and registration of human spinal vertebrae from CT scans using an optimised and improved 3D U-Net neural network architecture. The network is designed by incorporating residual and dense interconnections, followed by an extensive evaluation of different network setups by optimising the network components like activation functions, optimisers, and pooling operations. In addition, the network architecture is optimised for varying numbers of convolution layers per block and U-Net levels with fixed and cascading numbers of filters. For 3D virtual reality visualisation, the segmentation output of the improved 3D U-Net network is registered with the original scans through a corner point registration process. The registration takes into account the spatial coordinates of each segmented vertebra as a 3D volume and eight virtual fiducial markers to ensure alignment in all rotational planes. Trained on the VerSe'20 dataset, the proposed pipeline achieves a Dice score coefficient of 92.38% for vertebrae instance segmentation and a Hausdorff distance of 5.26 mm for vertebrae localisation on the VerSe'20 public test dataset, which outperforms many existing methods that participated in the VerSe'20 challenge. Integrated with Singular Health's MedVR software for virtual reality visualisation, the proposed solution has been deployed on standard edge-computing hardware in medical institutions. Depending on the scan size, the deployed solution takes between 90 and 210 s to label and segment vertebrae, including the cervical vertebrae. It is hoped that the acceleration of the segmentation and registration process will facilitate the easier preparation of future training datasets and benefit pre-surgical visualisation and planning.

Manually analyzing a series of MRI images to obtain information about the heart's motion is a time-consuming and labor-intensive task. Recently, many AI-driven tools have been used to automatically analyze cardiac MRI. However, it is still unknown whether the results generated by these tools are consistent. The aim of the present study was to investigate the agreement of AI-powered automated tools for measuring cine MRI-derived cardiac function and motion indices. Cine MRI datasets of 23 healthy volunteers (10 males, 32.7 ± 11.3 years) were processed using heart deformation analysis (HDA, Trufistrain) and Circle CVI 42. The left and right ventricular (LV/RV) end-diastolic volume (LVEDV and RVEDV), end-systolic volume (LVESV and RVESV), stroke volume (LVSV and RVSV), cardiac output (LVCO and RVCO), ejection fraction (LVEF and RVEF), LV mass (LVM), LV global strain, strain rate, displacement, and velocity were calculated without interventions. Agreements and discrepancies of indices acquired with the two tools were evaluated from various aspects using t-tests, Pearson correlation coefficient (r), interclass correlation coefficient (ICC), and coefficient of variation (CoV). Systematic biases for measuring cardiac function and motion indices were observed. In global cardiac function indices, LVEF (56.9% ± 6.4 vs. 57.8% ± 5.7, p = 0.433, r = 0.609, ICC = 0.757, CoV = 6.7%) and LVM (82.7 g ± 21.6 vs. 82.6 g ± 18.7, p = 0.988, r = 0.923, ICC = 0.956, CoV = 11.7%) acquired with HDA and Circle seemed to be exchangeable. Among cardiac motion indices, circumferential strain rate demonstrated good agreements between two tools (97 ± 14.6 vs. 97.8 ± 13.6, p = 0.598, r = 0.89, ICC = 0.943, CoV = 5.1%). Cine MRI-derived cardiac function and motion indices obtained using different AI-powered image processing tools are related but may also differ. Such variations should be considered when evaluating results sourced from different studies.

Breast cancer is a prevalent disease affecting millions of women worldwide, and early screening can significantly reduce mortality rates. Mammograms are widely used for screening, but manual readings can lead to misdiagnosis. Computer-assisted diagnosis can help physicians make faster, more accurate judgments, which benefits patients. However, segmenting and classifying breast masses in mammograms is challenging due to their similar shapes to the surrounding glands. Current target detection algorithms have limited applications and low accuracy. Automated segmentation of breast masses on mammograms is a significant research challenge due to its considerable classification and contouring. This study introduces the Bi-Contextual Breast Mass Segmentation Framework (Bi-CBMSegNet), a novel paradigm that enhances the precision and efficiency of breast mass segmentation within full-field mammograms. Bi-CBMSegNet employs an advanced encoder-decoder architecture comprising two distinct modules: the Global Feature Enhancement Module (GFEM) and the Local Feature Enhancement Module (LFEM). GFEM aggregates and assimilates features from all positions within the mammogram, capturing extensive contextual dependencies that facilitate the enriched representation of homogeneous regions. The LFEM module accentuates semantic information pertinent to each specific position, refining the delineation of heterogeneous regions. The efficacy of Bi-CBMSegNet has been rigorously evaluated on two publicly available mammography databases, demonstrating superior computational efficiency and performance metrics. The findings advocate for Bi-CBMSegNet to effectuate a significant leap forward in medical imaging, particularly in breast cancer screening, thereby augmenting the accuracy and efficacy of diagnostic and treatment planning processes.

In recent years, artificial intelligence has significantly advanced medical image segmentation. However, challenges remain, including efficient 3D medical image processing across diverse modalities and handling data variability. In this work, we introduce Hierarchical Soft Mixture-of-Experts (HoME), a two-level token-routing layer for efficient long-context modeling, specifically designed for 3D medical image segmentation. Built on the Mamba state-space model (SSM) backbone, HoME enhances sequential modeling through sparse, adaptive expert routing. The first stage employs a Soft Mixture-of-Experts (SMoE) layer to partition input sequences into local groups, routing tokens to specialized per-group experts for localized feature extraction. The second stage aggregates these outputs via a global SMoE layer, enabling cross-group information fusion and global context refinement. This hierarchical design, combining local expert routing with global expert refinement improves generalizability and segmentation performance, surpassing state-of-the-art results across datasets from the three most commonly used 3D medical imaging modalities and data quality.

Plain X-ray is one of the most common image modalities for clinical diagnosis (e.g. bone fracture, pneumonia, cancer screening, etc.). X-ray image segmentation is an essential step for many computer-aided diagnostic systems, yet it remains challenging. Deep-learning-based methods have achieved superior performance in medical image segmentation tasks but often require a large amount of high-quality annotated data for model training. Providing such an annotated dataset is not only time-consuming but also requires a high level of expertise. This is particularly challenging in wrist bone segmentation in X-rays, due to the interposition of multiple small carpal bones in the image. To overcome the data annotation issue, this work utilizes a large number of simulated X-ray images generated from Computed Tomography (CT) volumes with their corresponding 10 bone labels to train a deep learning-based model for wrist bone segmentation in real X-ray images. The proposed method was evaluated using both simulated images and real images. The method achieved Dice scores ranging from 0.80 to 0.92 for the simulated dataset generated from different view angles. Qualitative analysis of the segmentation results of the real X-ray images also demonstrated the superior performance of the trained model. The trained model and X-ray simulation code are freely available for research purposes: the link will be provided upon acceptance.

PurposeWe report on a deep learning-based approach to the segmentation of geographic atrophy (GA) in patients with advanced age-related macular degeneration (AMD). MethodThree-dimensional (3D) optical coherence tomography (OCT) data was collected from two instruments at two different retina practices. This totaled 367 and 348 volumes, respectively, of routinely collected clinical data. For all data, the accuracy of a 3D-to-2D segmentation model was assessed relative to ground-truth manual labeling. ResultsDice Similarity Scores (DSC) averaged 0.824 and 0.826 for each data set. Correlations (r2) between manual and automated areas were 0.883 and 0.906, respectively. The inclusion of near Infra-red imagery as an additional information channel to the algorithm did not notably improve performance. ConclusionAccurate assessment of GA in real-world clinical OCT data can be achieved using deep learning. In the advent of therapeutics to slow the rate of GA progression, reliable, automated assessment is a clinical objective and this work validates one such method.