Accurate segmentation of vascular structures in coronary angiography remains a core challenge in medical image analysis due to the complexity of elongated, thin, and low-contrast vessels. Classical convolutional neural networks (CNNs) often fail to preserve topological continuity, while recent Vision Transformer (ViT)-based models, although strong in global context modeling, lack precise boundary awareness. In this work, we introduce BAVT, a Boundary-Aware Vision Transformer, a ViT-based architecture enhanced with an edge-aware loss that explicitly guides the segmentation toward fine-grained vascular boundaries. Unlike hybrid transformer-CNN models, BAVT retains a minimal, scalable structure that is fully compatible with large-scale vision foundation model (VFM) pretraining. We validate our approach on the DCA-1 coronary angiography dataset, where BAVT achieves superior performance across medical image segmentation metrics outperforming both CNN and hybrid baselines. These results demonstrate the effectiveness of combining plain ViT encoders with boundary-aware supervision for clinical-grade vascular segmentation.

Retrospective single-institution study. To develop and validate an automated convolutional neural network (CNN) to measure the Cobb angle, T1 tilt angle, coronal balance, clavicular angle, height of the shoulders, T5-T12 Cobb angle, and sagittal balance for accurate scoliosis diagnosis. Scoliosis, characterized by a Cobb angle >10°, requires accurate and reliable measurements to guide treatment. Traditional manual measurements are time-consuming and have low interobserver and intraobserver reliability. While some automated tools exist, they often require manual intervention and focus primarily on the Cobb angle. In this study, we utilized four data sets comprising the anterior-posterior (AP) and lateral radiographs of 1682 patients with scoliosis. The CNN includes coarse segmentation, landmark localization, and fine segmentation. The measurements were evaluated using the dice coefficient, mean absolute error (MAE), and percentage of correct key-points (PCK) with a 3-mm threshold. An internal testing set, including 87 adolescent (7-16 yr) and 26 older adult patients (≥60 yr), was used to evaluate the agreement between automated and manual measurements. The automated measures by the CNN achieved high mean dice coefficients (>0.90), PCK of 89.7%-93.7%, and MAE for vertebral corners of 2.87-3.62 mm on AP radiographs. Agreement on the internal testing set for manual measurements was acceptable, with an MAE of 0.26 mm or degree-0.51 mm or degree for the adolescent subgroup and 0.29 mm or degree-4.93 mm or degree for the older adult subgroup on AP radiographs. The MAE for the T5-T12 Cobb angle and sagittal balance, on lateral radiographs, was 1.03° and 0.84 mm, respectively, in adolescents, and 4.60° and 9.41 mm, respectively, in older adults. Automated measurement time was significantly shorter compared with manual measurements. The deep learning automated system provides rapid, accurate, and reliable measurements for scoliosis diagnosis, which could improve clinical workflow efficiency and guide scoliosis treatment. Level III.

Convolutional Neural Networks (CNNs) have achieved remarkable success in breast ultrasound image segmentation, but they still face several challenges when dealing with breast lesions. Due to the limitations of CNNs in modeling long-range dependencies, they often perform poorly in handling issues such as similar intensity distributions, irregular lesion shapes, and blurry boundaries, leading to low segmentation accuracy. To address these issues, we propose the ThreeF-Net, a fine-grained feature fusion network. This network combines the advantages of CNNs and Transformers, aiming to simultaneously capture local features and model long-range dependencies, thereby improving the accuracy and stability of segmentation tasks. Specifically, we designed a Transformer-assisted Dual Encoder Architecture (TDE), which integrates convolutional modules and self-attention modules to achieve collaborative learning of local and global features. Additionally, we designed a Global Group Feature Extraction (GGFE) module, which effectively fuses the features learned by CNNs and Transformers, enhancing feature representation ability. To further improve model performance, we also introduced a Dynamic Fine-grained Convolution (DFC) module, which significantly improves lesion boundary segmentation accuracy by dynamically adjusting convolution kernels and capturing multi-scale features. Comparative experiments with state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that ThreeF-Net outperforms existing methods across multiple key evaluation metrics.

Accurate segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding anatomical structures is critical for diagnosis, treatment planning, and outcome assessment. This study proposes a deep learning-based framework to automate multi-class segmentation in CT images, comparing the performance of four state-of-the-art architectures. This retrospective multi-center study included 3265 patients from six institutions. Four deep learning models-UNet, nnU-Net, UNETR, and Swin-UNet-were trained using five-fold cross-validation on data from five centers and tested independently on a sixth center (n = 569). Preprocessing included intensity normalization, voxel resampling, and standardized annotation for six structures: PDAC lesion, pancreas, veins, arteries, pancreatic duct, and common bile duct. Evaluation metrics included Dice Similarity Coefficient (DSC), Intersection over Union (IoU), directed Hausdorff Distance (dHD), Average Symmetric Surface Distance (ASSD), and Volume Overlap Error (VOE). Statistical comparisons were made using Wilcoxon signed-rank tests with Bonferroni correction. Swin-UNet outperformed all models with a mean validation DSC of 92.4% and test DSC of 90.8%, showing minimal overfitting. It also achieved the lowest dHD (4.3 mm), ASSD (1.2 mm), and VOE (6.0%) in cross-validation. Per-class DSCs for Swin-UNet were consistently higher across all anatomical targets, including challenging structures like the pancreatic duct (91.0%) and bile duct (91.8%). Statistical analysis confirmed the superiority of Swin-UNet (p < 0.001). All models showed generalization capability, but Swin-UNet provided the most accurate and robust segmentation across datasets. Transformer-based architectures, particularly Swin-UNet, enable precise and generalizable multi-class segmentation of PDAC and surrounding anatomy. This framework has potential for clinical integration in PDAC diagnosis, staging, and therapy planning.

To evaluate a deep learning sequence-adaptive liver multiparametric MRI (mpMRI) assessment with validation in different populations using total and segmental T1 and T2 relaxation time maps. A neural network was trained to label liver segmental parenchyma and its vessels on noncontrast T1-weighted gradient-echo Dixon in-phase acquisitions on 200 liver mpMRI examinations. Then, 120 unseen liver mpMRI examinations of patients with primary sclerosing cholangitis or healthy controls were assessed by coregistering the labels to noncontrast and contrast-enhanced T1 and T2 relaxation time maps for optimization and internal testing. The algorithm was externally tested in a segmental and total liver analysis of previously unseen 65 patients with biopsy-proven liver fibrosis and 25 healthy volunteers. Measured relaxation times were compared to manual measurements using intraclass correlation coefficient (ICC) and Wilcoxon test. Comparison of manual and deep learning-generated segmental areas on different T1 and T2 maps was excellent for segmental (ICC = 0.95 ± 0.1; p < 0.001) and total liver assessment (0.97 ± 0.02, p < 0.001). The resulting median of the differences between automated and manual measurements among all testing populations and liver segments was 1.8 ms for noncontrast T1 (median 835 versus 842 ms), 2.0 ms for contrast-enhanced T1 (median 518 versus 519 ms), and 0.3 ms for T2 (median 37 versus 37 ms). Automated quantification of liver mpMRI is highly effective across different patient populations, offering excellent reliability for total and segmental T1 and T2 maps. Its scalable, sequence-adaptive design could foster comprehensive clinical decision-making. The proposed automated, sequence-adaptive algorithm for total and segmental analysis of liver mpMRI may be co-registered to any combination of parametric sequences, enabling comprehensive quantitative analysis of liver mpMRI without sequence-specific training. A deep learning-based algorithm automatically quantified segmental T1 and T2 relaxation times in liver mpMRI. The two-step approach of segmentation and co-registration allowed to assess arbitrary sequences. The algorithm demonstrated high reliability with manual reader quantification. No additional sequence-specific training is required to assess other parametric sequences. The DL algorithm has the potential to enhance individual liver phenotyping.

Segmentation of target volumes and organs at risk on computed tomography (CT) images constitutes an important step in the radiotherapy workflow. Artificial intelligence-based methods have significantly improved organ segmentation in medical images. Automatic segmentations are frequently evaluated using geometric metrics. Before a clinical implementation in the radiotherapy workflow, automatic segmentations must also be evaluated by clinicians. The aim of this study was to investigate the correlation between geometric metrics used for segmentation evaluation and the assessment performed by clinicians. In this study, we used the U-Net model to segment the liver in CT images from a publicly available dataset. The model's performance was evaluated using two geometric metrics: the Dice similarity coefficient and the Hausdorff distance. Additionally, a qualitative evaluation was performed by clinicians who reviewed the automatic segmentations to rate their clinical acceptability for use in the radiotherapy workflow. The correlation between the geometric metrics and the clinicians' evaluations was studied. The results showed that while the Dice coefficient and Hausdorff distance are reliable indicators of segmentation accuracy, they do not always align with clinician segmentation. In some cases, segmentations with high Dice scores still required clinician corrections before clinical use in the radiotherapy workflow. This study highlights the need for more comprehensive evaluation metrics beyond geometric measures to assess the clinical acceptability of artificial intelligence-based segmentation. Although the deep learning model provided promising segmentation results, the present study shows that standardized validation methodologies are crucial for ensuring the clinical viability of automatic segmentation systems.

Brain tumors, particularly glioblastoma multiforme, are considered one of the most threatening types of tumors in neuro-oncology. Segmenting brain tumors is a crucial part of medical imaging. It plays a key role in diagnosing conditions, planning treatments, and keeping track of patients' progress. This paper presents a novel lightweight deep convolutional neural network (CNN) model specifically designed for accurate and efficient brain tumor segmentation from magnetic resonance imaging (MRI) scans. Our model leverages a streamlined architecture that reduces computational complexity while maintaining high segmentation accuracy. We have introduced several novel approaches, including optimized convolutional layers that capture both local and global features with minimal parameters. A layerwise adaptive weighting feature fusion technique is implemented that enhances comprehensive feature representation. By incorporating shifted windowing, the model achieves better generalization across data variations. Dynamic weighting is introduced in skip connections that allows backpropagation to determine the ideal balance between semantic and positional features. To evaluate our approach, we conducted experiments on publicly available MRI datasets and compared our model against state-of-the-art segmentation methods. Our lightweight model has an efficient architecture with 1.45 million parameters - 95% fewer than nnUNet (30.78M), 91% fewer than standard UNet (16.21M), and 85% fewer than a lightweight hybrid CNN-transformer network (Liu et al., 2024) (9.9M). Coupled with a 4.9× faster GPU inference time (0.904 ± 0.002 s vs. nnUNet's 4.416 ± 0.004 s), the design enables real-time deployment on resource-constrained devices while maintaining competitive segmentation accuracy. Code is available at: FFLUNet.

BackgroundIntracranial hemorrhage (ICH), whether spontaneous or traumatic, is a neurological emergency with high morbidity and mortality. Accurate assessment of severity is essential for neurosurgical decision-making. This study aimed to develop and evaluate a fully automated, deep learning-based tool for the standardized assessment of ICH severity, based on the segmentation of the hemorrhage and intracranial structures, and the computation of an objective severity index. MethodsNon-contrast cranial CT scans from patients with spontaneous or traumatic ICH were retrospectively collected from public datasets and a tertiary care center. Deep learning models were trained to segment hemorrhages and intracranial structures. These segmentations were used to compute a severity index reflecting bleeding burden and mass effect through volumetric relationships. Segmentation performance was evaluated on a hold-out test cohort. In a prospective cohort, the severity index was assessed in relation to expert-rated CT severity, clinical outcomes, and the need for urgent neurosurgical intervention. ResultsA total of 1,110 non-contrast cranial CT scans were analyzed, 900 from the retrospective cohort and 200 from the prospective evaluation cohort. The binary segmentation model achieved a median Dice score of 0.90 for total hemorrhage. The multilabel model yielded Dice scores ranging from 0.55 to 0.94 across hemorrhage subtypes. The severity index significantly correlated with expert-rated CT severity (p < 0.001), the modified Rankin Scale (p = 0.007), and the Glasgow Outcome Scale-Extended (p = 0.039), and independently predicted the need for urgent surgery (p < 0.001). A threshold [~]300 was identified as a decision point for surgical management (AUC = 0.83). ConclusionWe developed a fully automated and openly accessible pipeline for the analysis of non-contrast cranial CT in intracranial hemorrhage. It computes a novel index that objectively quantifies hemorrhage severity and is significantly associated with clinically relevant outcomes, including the need for urgent neurosurgical intervention.

Differences in CT-based body composition (BC) have been observed by race. We sought to investigate whether indices reporting census block group-level disadvantage, area deprivation index (ADI) and social vulnerability index (SVI), age, sex, and/or clinical factors could explain race-based differences in body composition. The first abdominal CT exams for patients in Durham County at a single institution in 2020 were analyzed using a fully automated and open-source deep learning BC analysis workflow to generate cross-sectional areas for skeletal muscle (SMA), subcutaneous fat (SFA), and visceral fat (VFA). Patient level demographic and clinical data were gathered from the electronic health record. State ADI ranking and SVI values were linked to each patient. Univariable and multivariable models were created to assess the association of demographics, ADI, SVI, and other relevant clinical factors with SMA, SFA, and VFA. 5,311 patients (mean age, 57.4 years; 55.5% female, 46.5% Black; 39.5% White 10.3% Hispanic) were included. At univariable analysis, race, ADI, SVI, sex, BMI, weight, and height were significantly associated with all body compartments (SMA, SFA, and VFA, all p<0.05). At multivariable analyses adjusted for patient characteristics and clinical comorbidities, race remained a significant predictor, whereas ADI did not. SVI was significant in a multivariable model with SMA.

Despite the advances in automated medical image segmentation, AI models still underperform in various clinical settings, challenging real-world integration. In this multicenter evaluation, we analyzed 20 state-of-the-art mandibular segmentation models across 19,218 segmentations of 1,000 clinically resampled CT/CBCT scans. We show that segmentation accuracy varies by up to 25% depending on socio-technical factors such as voxel size, bone orientation, and patient conditions such as osteosynthesis or pathology. Higher sharpness, isotropic smaller voxels, and neutral orientation significantly improved results, while metallic osteosynthesis and anatomical complexity led to significant degradation. Our findings challenge the common view of AI models as "plug-and-play" tools and suggest evidence-based optimization recommendations for both clinicians and developers. This will in turn boost the integration of AI segmentation tools in routine healthcare.