用辅助分类提升肋骨骨折分割准确率
Leveraging Auxiliary Classification for Rib Fracture Segmentation
- 引入辅助分类任务区分骨折与非骨折区域
- 在RibFrac数据集上分割性能显著提升
- 适合医学图像分割与放射诊断研究者
胸部创伤常导致肋骨骨折，需快速准确诊断以有效治疗。然而，在肋骨CT扫描中检测骨折面临巨大挑战，需逐序列分析大量图像切片。尽管自动化骨折分割算法已有显著进展，但骨折形状和大小的多样性仍是主要难题。为此，本研究提出一种基于辅助分类任务的深度学习模型，通过区分骨折肋骨与非骨折区域（包括正常肋骨和周围组织）来增强特征表示。该辅助任务聚焦于从CT图像中提取的图像块，旨在提升瓶颈层的特征表达能力。在RibFrac数据集上的实验表明，该方法显著改善了分割性能。
原文摘要 · Abstract (English)
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.
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