arXiv:2601.15235v3 Announce Type: replace-cross Abstract: Cervical spine fractures demand rapid and accurate diagnosis for effective clinical management. This study presents an automated, end-to-end pipeline for fracture detection across cervical vertebrae (C1--C7) that assesses the feasibility of fracture recognition from vertebra-level volumes of interest extracted using estimated 3D masks derived from fused orthogonal 2D segmentations. Unlike traditional 3D methods, our approach approximates 3D volumes via optimized 2D axial, sagittal, and coronal projections to reduce input dimensionality of intermediate pre-processing steps while maintaining high diagnostic performance for downstream fracture classification. First, spine regions of interest are localized from multi-view variance projections using a YOLOv8 detector, achieving a 3D mean Intersection over Union of 94.45%. Next, multi-label vertebra segmentation is performed using a DenseNet121-Unet architecture on energy-based sagittal and coronal projections, attaining a mean Dice score of 87.86%. The orthogonal 2D masks are then fused to reconstruct an estimated 3D mask for each vertebra, which is used to extract volumes of interest from the original CT. These extracted vertebra volumes are subsequently analyzed for fractures using an ensemble of 2.5D spatio-sequential CNN-Transformer models, yielding vertebra-level and patient-level F1 scores of 68.15 and 82.26, with area under the receiver operating characteristic curve scores of 91.62 and 83.04, respectively. The framework is further validated through an explainability study using saliency map visualizations and an interobserver variability analysis. Overall, the results indicate that this projection-based strategy delivers clinically relevant performance comparable to expert radiologists, while reducing the dimensionality of intermediate stages, supporting its potential for practical deployment.

arXiv:2510.27315v2 Announce Type: replace-cross Abstract: Early detection of coronary artery disease (CAD) is critical for reducing mortality and improving patient treatment planning. While angiographic image analysis from X-rays is a common and cost-effective method for identifying cardiac abnormalities, including stenotic coronary arteries, poor image quality can significantly impede clinical diagnosis. We present the Coronary Artery Segmentation and Refinement Network (CASR-Net), a three-stage pipeline comprising image preprocessing, segmentation, and refinement. A novel multichannel preprocessing strategy combining CLAHE and an improved Ben Graham method provides incremental gains, increasing Dice Score Coefficient (DSC) by 0.31-0.89% and Intersection over Union (IoU) by 0.40-1.16% compared with using the techniques individually. The core innovation is a segmentation network built on a UNet with a DenseNet121 encoder and a Self-organized Operational Neural Network (Self-ONN) based decoder, which preserves the continuity of narrow and stenotic vessel branches. A final contour refinement module further suppresses false positives. Evaluated with 5-fold cross-validation on a combination of two public datasets that contain both healthy and stenotic arteries, CASR-Net outperformed several state-of-the-art models, achieving an IoU of 61.43%, a DSC of 76.10%, and clDice of 79.36%. These results highlight a robust approach to automated coronary artery segmentation, offering a valuable tool to support clinicians in diagnosis and treatment planning.