Leveraging Auxiliary Classification for Rib Fracture Segmentation

TL;DR

This paper presents a deep learning model with an auxiliary classification task that improves rib fracture segmentation accuracy on CT scans by better distinguishing fractured ribs from non-fractured regions, addressing shape and size variability.

Contribution

The study introduces a novel auxiliary classification approach within a deep learning model to enhance rib fracture segmentation accuracy on CT images.

Findings

Significant improvement in segmentation performance on RibFrac dataset

Effective differentiation between fractured and non-fractured regions

Enhanced feature representation at the model's bottleneck layer

Abstract

Thoracic trauma often results in rib fractures, which demand swift and accurate diagnosis for effective treatment. However, detecting these fractures on rib CT scans poses considerable challenges, involving the analysis of many image slices in sequence. Despite notable advancements in algorithms for automated fracture segmentation, the persisting challenges stem from the diverse shapes and sizes of these fractures. To address these issues, this study introduces a sophisticated deep-learning model with an auxiliary classification task designed to enhance the accuracy of rib fracture segmentation. The auxiliary classification task is crucial in distinguishing between fractured ribs and negative regions, encompassing non-fractured ribs and surrounding tissues, from the patches obtained from CT scans. By leveraging this auxiliary task, the model aims to improve feature representation at the bottleneck layer by highlighting the regions of interest. Experimental results on the RibFrac dataset demonstrate significant improvement in segmentation performance.