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.

翻译：胸部创伤常导致肋骨骨折，需要快速准确的诊断以实现有效治疗。然而，在肋骨CT扫描中检测这些骨折存在相当大的挑战，涉及对大量连续图像切片的分析。尽管自动骨折分割算法已取得显著进展，但骨折形态和大小的多样性仍是持续存在的难题。为解决这些问题，本研究引入了一种结合辅助分类任务的深度学习模型，旨在提升肋骨骨折分割的准确性。该辅助分类任务的核心在于区分从CT扫描获取的图像块中的骨折肋骨与阴性区域（包括非骨折肋骨及周围组织）。通过利用此辅助任务，模型旨在通过突出关注区域来改善瓶颈层的特征表示。在RibFrac数据集上的实验结果表明，分割性能得到显著提升。