Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor.
Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor.
复制标题
使用强度分布监督改进 CT 扫描中的小病灶分割：在小肠类癌中的应用。
DOI：
10.1117/12.2651979
复制
发表时间：
2023
期刊：
影响因子：
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通讯作者：
Summers,RonaldM
中科院分区：
文献类型：
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作者：
Shin,SeungYeon;Shen,ThomasC;Wank,StephenA;Summers,RonaldM
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。改进小病变分割的一种方法是缩小感兴趣区域并以更高的灵敏度对其进行检查，而不是对整个区域进行检查。它通常实现为器官和病灶的顺序或联合分割，这需要对器官分割进行额外的监督。相反，我们建议利用目标病变的强度分布，无需额外的标记成本，即可有效地将病变可能位于的区域与背景分开。它作为辅助任务纳入网络训练中。我们将所提出的方法应用于 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都有所改善（全局、每个病例和每个肿瘤 Dice 评分分别为 33.5% → 38.2%、41.3% → 47.8%、30.0% → 35.9%。），这证明了我们想法的有效性。我们的方法可以成为在网络训练中明确纳入目标强度分布信息的一种选择。
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.