Intracerebral hemorrhage (ICH) is a medical emergency that demands rapid and accurate diagnosis for optimal patient management. Hemorrhagic lesions' segmentation on CT scans is a necessary first step for acquiring quantitative imaging data that are becoming increasingly useful in the clinical setting. However, traditional manual segmentation is time-consuming and prone to inter-rater variability, creating a need for automated solutions. This study introduces a novel approach combining advanced deep learning models to segment extensive and morphologically variable ICH lesions in non-contrast CT scans. We propose a two-step methodology that begins with a user-defined loose bounding box around the lesion, followed by a fine-tuned YOLOv8-S object detection model to generate precise, slice-specific bounding boxes. These bounding boxes are then used to prompt the Medical Segment Anything Model for accurate lesion segmentation. Our pipeline achieves high segmentation accuracy with minimal supervision, demonstrating strong potential as a practical alternative to task-specific models. We evaluated the model on a dataset of 252 CT scans demonstrating high performance in segmentation accuracy and robustness. Finally, the resulting segmentation tool is integrated into a user-friendly web application prototype, offering clinicians a simple interface for lesion identification and radiomic quantification.

This work presents the results of a methodological transfer from remote sensing to healthcare, adapting AMBER -- a transformer-based model originally designed for multiband images, such as hyperspectral data -- to the task of 3D medical datacube segmentation. In this study, we use the AMBER architecture with Adaptive Fourier Neural Operators (AFNO) in place of the multi-head self-attention mechanism. While existing models rely on various forms of attention to capture global context, AMBER-AFNO achieves this through frequency-domain mixing, enabling a drastic reduction in model complexity. This design reduces the number of trainable parameters by over 80% compared to UNETR++, while maintaining a FLOPs count comparable to other state-of-the-art architectures. Model performance is evaluated on two benchmark 3D medical datasets -- ACDC and Synapse -- using standard metrics such as Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), demonstrating that AMBER-AFNO achieves competitive or superior accuracy with significant gains in training efficiency, inference speed, and memory usage.

Accurate and efficient brain tumor segmentation remains a critical challenge in neuroimaging due to the heterogeneous nature of tumor subregions and the high computational cost of volumetric inference. In this paper, we propose EfficientGFormer, a novel architecture that integrates pretrained foundation models with graph-based reasoning and lightweight efficiency mechanisms for robust 3D brain tumor segmentation. Our framework leverages nnFormer as a modality-aware encoder, transforming multi-modal MRI volumes into patch-level embeddings. These features are structured into a dual-edge graph that captures both spatial adjacency and semantic similarity. A pruned, edge-type-aware Graph Attention Network (GAT) enables efficient relational reasoning across tumor subregions, while a distillation module transfers knowledge from a full-capacity teacher to a compact student model for real-time deployment. Experiments on the MSD Task01 and BraTS 2021 datasets demonstrate that EfficientGFormer achieves state-of-the-art accuracy with significantly reduced memory and inference time, outperforming recent transformer-based and graph-based baselines. This work offers a clinically viable solution for fast and accurate volumetric tumor delineation, combining scalability, interpretability, and generalization.

Accurate segmentation of renal arteries from X-ray angiography videos is crucial for evaluating renal sympathetic denervation (RDN) procedures but remains challenging due to dynamic changes in contrast concentration and vessel morphology across frames. The purpose of this study is to propose TCA-Net, a deep learning model that improves segmentation consistency by leveraging local and global contextual information in angiography videos. Our approach utilizes a novel deep learning framework that incorporates two key modules: a local temporal window vessel enhancement module and a global vessel refinement module (GVR). The local module fuses multi-scale temporal-spatial features to improve the semantic representation of vessels in the current frame, while the GVR module integrates decoupled attention strategies (video-level and object-level attention) and gating mechanisms to refine global vessel information and eliminate redundancy. To further improve segmentation consistency, a temporal perception consistency loss function is introduced during training. We evaluated our model using 195 renal artery angiography sequences for development and tested it on an external dataset from 44 patients. The results demonstrate that TCA-Net achieves an F1-score of 0.8678 for segmenting renal arteries, outperforming existing state-of-the-art segmentation methods. We present TCA-Net, a deep learning-based model that significantly improves segmentation consistency for renal artery angiography videos. By effectively leveraging both local and global temporal contextual information, TCA-Net outperforms current methods and provides a reliable tool for assessing RDN procedures.

In study, we aimed to dosimetrically evaluate the usability of a new generation autocontouring algorithm (DirectORGANS) that automatically identifies organs and contours them directly in the computed tomography (CT) simulator before creating prostate radiotherapy plans. The CT images of 10 patients were used in this study. The prostates, bladder, rectum, and femoral heads of 10 patients were automatically contoured based on DirectORGANS algorithm at the CT simulator. On the same CT image sets, the same target volumes and contours of organs at risk were manually contoured by an experienced physician using MRI images and used as a reference structure. The doses of manually delineated contours of the target volume and organs at risk and the doses of auto contours of the target volume and organs at risk were obtained from the dose volume histogram of the same plan. Conformity index (CI) and homogeneity index (HI) were calculated to evaluate the target volumes. In critical organ structures, V_60, V_65, V₇₀ for the rectum, V_65, V70, V75, and V₈₀ for the bladder, and maximum doses for femoral heads were evaluated. The Mann-Whitney U test was used for statistical comparison with statistical package SPSS (P < 0.05). Compared to the doses of the manual contours (MC) with auto contours (AC), there was no significant difference between the doses of the organs at risk. However, there were statistically significant differences between HI and CI values due to differences in prostate contouring (P < 0.05). The study showed that the need for clinicians to edit target volumes using MRI before treatment planning. However, it demonstrated that delineating organs at risk was used safely without the need for correction. DirectORGANS algorithm is suitable for use in RT planning to minimize differences between physicians and shorten the duration of this contouring step.

PurposeTo develop and validate an artificial intelligence (AI)-based model that automatically measures choroidal mass dimensions on B{square}scan ophthalmic ultrasound still images and cine loops. DesignRetrospective diagnostic accuracy study with internal and external validation. ParticipantsThe dataset included 1,822 still images and 283 cine loops of choroidal masses for model development and testing. An additional 182 still images were used for external validation, and 302 control images with other diagnoses were included to assess specificity MethodsA deep convolutional neural network (CNN) based on the U-Net architecture was developed to automatically measure the apical height and basal diameter of choroidal masses on B-scan ultrasound. All still images were manually annotated by expert graders and reviewed by a senior ocular oncologist. Cine loops were analyzed frame by frame and the frame with the largest detected mass dimensions was selected for evaluation. Outcome MeasuresThe primary outcome was the models measurement accuracy, defined by the mean absolute error (MAE) in millimeters, compared to expert manual annotations, for both apical height and basal diameter. Secondary metrics included the Dice coefficient, coefficient of determination (R2), and mean pixel distance between predicted and reference measurements. ResultsOn the internal test set of still images, the model successfully detected the tumor in 99.7% of cases. The mean absolute error (MAE) was 0.38 {+/-} 0.55 mm for apical height (95.1% of measurements <1 mm of the expert annotation) and was 0.99 {+/-} 1.15 mm for basal diameter (64.4% of measurements <1 mm). Linear agreement between predicted and reference measurements was strong, with R2 values of 0.74 for apical height and 0.89 for basal diameter. When applied to the control set of 302 control images, the model demonstrated a moderate false positive rate. On the external validation set, the model maintained comparable accuracy. Among the cine loops, the model detected tumors in 89.4% of cases with comparable accuracy. ConclusionDeep learning can deliver fast, reproducible, millimeter{square}level measurements of choroidal mass dimensions with robust performance across different mass types and imaging sources. These findings support the potential clinical utility of AI-assisted measurement tools in ocular oncology workflows.

Accurate quantification of the extent of bronchial damage across various airway levels in chronic obstructive pulmonary disease (COPD) remains a challenge. In this study, artificial intelligence (AI) was employed to develop an airway segmentation model to investigate the morphological changes of the central and peripheral airways in COPD patients and the effects of these airway changes on pulmonary function classification and acute COPD exacerbations. Clinical data from a total of 340 patients with COPD and 73 healthy volunteers were collected and compiled. An AI-driven airway segmentation model was constructed using Convolutional Neural Regressor (CNR) and Airway Transfer Network (ATN) algorithms. The efficacy of the model was evaluated through support vector machine (SVM) and random forest regression approaches. The area under the receiver operating characteristic (ROC) curve (AUC) of the SVM in evaluating the COPD airway segmentation model was 0.96, with a sensitivity of 97% and a specificity of 92%, however, the AUC value of the SVM was 0.81 when it was replaced the healthy group by non-COPD outpatients. Compared with the healthy group, the grade and the total number of airway segmentation were decreased and the diameters of the right main bronchus and bilateral lobar bronchi of patients with COPD were smaller and the airway walls were thinner (all P < 0.01). However, the diameters of the subsegmental and small airway bronchi were increased, and airway walls were thickened, and the arc lengths were shorter ( all P < 0.01), especially in patients with severe COPD (all P < 0.05). Correlation and regression analysis showed that FEV1%pre was positively correlated with the diameters and airway wall thickness of the main and lobar airway, and the arc lengths of small airway bronchi (all P < 0.05). Airway wall thickness of the subsegment and small airway were found to have the greatest impact on the frequency of COPD exacerbations. Artificial intelligence lung CT airway segmentation model is a non-invasive quantitative tool for measuring chronic obstructive pulmonary disease. The main changes in COPD patients are that the central airway diameter becomes narrower and the thickness becomes thinner. The arc length of the peripheral airway becomes shorter, and the diameter and airway wall thickness become larger, which is more obvious in severe patients. Pulmonary function classification and small and medium airway dysfunction are also affected by the diameter, thickness and arc length of large and small airways. Small airway remodeling is more significant in acute exacerbations of COPD.

Intracranial aneurysms (IAs) are abnormal dilations of cerebral blood vessels that, if ruptured, can lead to life-threatening consequences. However, their small size and soft contrast in radiological scans often make it difficult to perform accurate and efficient detection and morphological analyses, which are critical in the clinical care of the disorder. Furthermore, the lack of large public datasets with voxel-wise expert annotations pose challenges for developing deep learning algorithms to address the issues. Therefore, we proposed a novel weakly supervised 3D multi-task UNet that integrates vesselness priors to jointly perform aneurysm detection and segmentation in time-of-flight MR angiography (TOF-MRA). Specifically, to robustly guide IA detection and segmentation, we employ the popular Frangi's vesselness filter to derive soft cerebrovascular priors for both network input and an attention block to conduct segmentation from the decoder and detection from an auxiliary branch. We train our model on the Lausanne dataset with coarse ground truth segmentation, and evaluate it on the test set with refined labels from the same database. To further assess our model's generalizability, we also validate it externally on the ADAM dataset. Our results demonstrate the superior performance of the proposed technique over the SOTA techniques for aneurysm segmentation (Dice = 0.614, 95%HD =1.38mm) and detection (false positive rate = 1.47, sensitivity = 92.9%).

The lumbar spine and pelvis are critical stress-bearing structures of the human body, and their rapid and accurate segmentation plays a vital role in clinical diagnosis and intervention. However, conventional CT imaging poses significant challenges due to the low contrast of sacral and bilateral hip tissues and the complex and highly similar intervertebral space structures within the lumbar spine. To address these challenges, we propose a general-purpose segmentation network that integrates a cross-scale feature fusion strategy with a linear self-attention mechanism. The proposed network effectively extracts multi-scale features and fuses them along the channel dimension, enabling both structural and boundary information of lumbar and pelvic regions to be captured within the encoder-decoder architecture.Furthermore, we introduce a linear mapping strategy to approximate the traditional attention matrix with a low-rank representation, allowing the linear attention mechanism to significantly reduce computational complexity while maintaining segmentation accuracy for vertebrae and pelvic bones. Comparative and ablation experiments conducted on the CTSpine1K and CTPelvic1K datasets demonstrate that our method achieves improvements of 1.5% in Dice Similarity Coefficient (DSC) and 2.6% in Hausdorff Distance (HD) over state-of-the-art models, validating the effectiveness of our approach in enhancing boundary segmentation quality and segmentation accuracy in homogeneous anatomical regions.

Cardiovascular diseases are the leading cause of death in the world, with coronary artery disease being the most prevalent. Coronary artery calcifications are critical biomarkers for cardiovascular disease, and their quantification via non-contrast computed tomography is a widely accepted and heavily employed technique for risk assessment. Manual segmentation of these calcifications is a time-consuming task, subject to variability. State-of-the-art methods often employ convolutional neural networks for an automated approach. However, there is a lack of studies that perform these segmentations with 3D architectures that can gather important and necessary anatomical context to distinguish the different coronary arteries. This paper proposes a novel and automated approach that uses a lightweight three-dimensional convolutional neural network to perform efficient and accurate segmentations and calcium scoring. Results show that this method achieves Dice score coefficients of 0.93 ± 0.02, 0.93 ± 0.03, 0.84 ± 0.02, 0.63 ± 0.06 and 0.89 ± 0.03 for the foreground, left anterior descending artery (LAD), left circumflex artery (LCX), left main artery (LM) and right coronary artery (RCA) calcifications, respectively, outperforming other state-of-the-art architectures. An external cohort validation also showed the generalization of this method's performance and how it can be applied in different clinical scenarios. In conclusion, the proposed lightweight 3D convolutional neural network demonstrates high efficiency and accuracy, outperforming state-of-the-art methods and showcasing robust generalization potential.