Segmentation of Calcified Lesions in Lower Limb Artery CTA

Due to the growing incidence of vascular-related conditions, the accurate identification of arterial calcifications has become a critical step in preoperative planning for intravascular interventions. While coronary calcification segmentation methods have been extensively studied, calcification detection in lower limb arteries remains underexplored due to complex anatomical structures and insufficient public datasets. This paper applied a method based on convolutional neural network (CNN) to segment calcified plaques in lower limb CT angiography (CTA) using the nnUNet framework. A custom dataset of 23 annotated lower limb CTA scans was prepared, and three nnUNet variants(2D UNet, 3D fullres UNet, and 3D lowres UNet) were evaluated. In the experiment, the performance of 3D fullres UNet was better than that of 2D UNet and 3D lowres UNet, with a Pseudo Dice of 0.4793 and an IoU of 0.3442. The results suggest that 3D fullres models are more effective in capturing the spatial complexity of lower limb vascular calcifications and hold promise for improving automated vascular assessment in clinical workflows.