Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor.

Seung Yeon Shin, Thomas C Shen, Stephen A Wank, Ronald M Summers
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引用次数: 1

Abstract

Finding small lesions is very challenging due to lack of noticeable features, severe class imbalance, as well as the size itself. One approach to improve small lesion segmentation is to reduce the region of interest and inspect it at a higher sensitivity rather than performing it for the entire region. It is usually implemented as sequential or joint segmentation of organ and lesion, which requires additional supervision on organ segmentation. Instead, we propose to utilize an intensity distribution of a target lesion at no additional labeling cost to effectively separate regions where the lesions are possibly located from the background. It is incorporated into network training as an auxiliary task. We applied the proposed method to segmentation of small bowel carcinoid tumors in CT scans. We observed improvements for all metrics (33.5% → 38.2%, 41.3% → 47.8%, 30.0% → 35.9% for the global, per case, and per tumor Dice scores, respectively.) compared to the baseline method, which proves the validity of our idea. Our method can be one option for explicitly incorporating intensity distribution information of a target in network training.

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利用强度分布监测改进CT扫描小病变分割:在小肠类癌中的应用。
由于缺乏明显的特征,严重的类别不平衡以及大小本身,寻找小病变非常具有挑战性。一种改进小病灶分割的方法是减少感兴趣的区域并以更高的灵敏度检查它,而不是对整个区域进行检查。它通常以器官和病变的顺序或联合分割来实现,这需要对器官分割进行额外的监督。相反,我们建议在没有额外标记成本的情况下利用目标病变的强度分布来有效地将病变可能位于的区域与背景分开。作为辅助任务纳入网络训练。我们将该方法应用于CT扫描中小肠类癌的分割。与基线方法相比,我们观察到所有指标的改善(全球,每个病例和每个肿瘤的Dice得分分别为33.5%→38.2%,41.3%→47.8%,30.0%→35.9%),这证明了我们想法的有效性。我们的方法可以作为在网络训练中明确纳入目标强度分布信息的一种选择。
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