Deep learning workflow for 3D calcification segmentation in cone beam computed tomographic images

Calcium salt deposits, commonly referred to as calcifications, can form in various body tissues. Among them, calcifications in the cervical carotid arteries are of particular clinical significance due to their strong association with cardiovascular disease. These calcifications are often detected as incidental findings, that is, unexpected observations that may carry greater clinical importance than the original purpose of the imaging. Given their diagnostic relevance, automated detection and segmentation of such calcifications in medical images are critical for enabling early diagnosis and improving clinical decision-making. Despite their importance, detecting carotid artery calcifications presents several challenges: their small size, sparse distribution, and visual similarity to surrounding anatomical structures make them difficult to identify accurately. To address these challenges, this study proposes a multi-stage workflow for segmenting carotid artery calcifications. First, we introduce a novel 3D dataset comprising Cone Beam Computed Tomography (CBCT) scans that contain incidental neck calcifications. For the segmentation task, we adapt 3D UNETR, a vision transformer-based model. We then employ a 3D Grad-CAM technique to generate class activation maps, which are subsequently used in a post-processing step to refine the segmentation results. The proposed workflow achieves a Dice coefficient of 64% for segmentation, despite calcifications occupying only 0.04% of the total image volume. Beyond its strong performance on such sparse targets, the approach remains computationally efficient, with modest memory requirements and fast inference times, making it practical for integration into clinical imaging workflows.