Automatic rib segmentation and sequential labeling via multi-axial slicing and 3D reconstruction

Abstract

Radiologists often inspect hundreds of two-dimensional computed-tomography (CT) images to accurately locate lesions and make diagnoses, by classifying and labeling the ribs. However, this task is repetitive and time consuming. To effectively address this problem, we propose a multi-axial rib segmentation and sequential labeling (MARSS) method. First, we slice the CT volume into sagittal, frontal, and transverse planes for segmentation. The segmentation masks generated for each plane are then reconstructed into a single 3D segmentation mask using binarization techniques. After separating the left and right rib volumes from the entire CT volume, we cluster the connected components identified as bones and sequentially assign labels to each rib. The segmentation and sequential labeling performance of this method outperformed existing methods by up to 4.2%. The proposed automatic rib sequential labeling method enhances the efficiency of radiologists. In addition, this method provides an extended opportunity for advancements not only in rib segmentation but also in bone-fracture detection and lesion-diagnosis research.