基于深度学习的腹部CT图像中肠梗阻的检测与定位

中文摘要 AI 辅助

肠梗阻是一种常见且可能危及生命的胃肠道疾病。面对不断增加的诊断工作量，CT扫描上肠梗阻的自动诊断通过加速检测和改善患者预后为放射科医生提供支持。在这项工作中，我们提出了一个具有多任务目标的深度学习框架，该框架联合检测肠梗阻并定位其过渡区。此外，我们用一种本质上可解释的分类方法扩展了该方法，该方法在切片内定位疑似过渡点。它通过学习一个概率选择掩码来实现，该掩码仅基于一个小图像区域忠实地为分类器的预测提供依据。所提出的方法在一个包含1427例腹部CT的内部数据集上进行了评估。该模型在梗阻检测测试中的准确率达到93%，在过渡区定位的Hit@10为95%。作为第一种可靠地定位过渡区的方法，这标志着朝着自动识别这一关键临床标志迈出了重要一步。

英文摘要

Bowel obstruction is a common and potentially life-threatening gastrointestinal condition. In the face of rising diagnostic workloads, the automated diagnosis of bowel obstruction on CT scans supports radiologists by accelerating detection and improving patient outcomes. In this work, we propose a deep learning framework with a multi-task objective that jointly detects bowel obstruction and localizes its transition zone. Additionally, we extend the method with an inherently interpretable classification method that locates the suspected transition point within a slice. It does so by learning a probabilistic selection mask that faithfully bases the classifier's prediction solely on a small image region. The proposed method is evaluated on an internal dataset comprising 1,427 abdominal CTs. Here, the model achieves an obstruction detection test accuracy of 93% and a Hit@10 transition zone localization of 95%. As the first method to reliably localize the transition zone, this marks a significant step towards the automated identification of this critical clinical landmark.

发表机构

• Department of Computer Science ETH Zurich（苏黎世联邦理工学院计算机科学系）
• Department of Radiology Kantonsspital Baden affiliated Hospital for Research and Teaching of the Faculty of Medicine of the University of Zurich（苏黎世大学医学院附属巴登州立医院放射科（巴登大学苏黎世医学院研究与教学附属医院））
• Department of Forensic Medicine Zurich University of Zurich（苏黎世大学法医学系）

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