论文标题
使用强度分布监督改善CT扫描中的小病变分割：应用于小肠癌肿瘤
Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor
论文作者
论文摘要
由于缺乏明显的特征，严重的阶级失衡以及大小本身，找到小病变非常具有挑战性。改善小病变细分的一种方法是减少感兴趣的区域并以更高的灵敏度进行检查，而不是为整个区域进行检查。通常将其作为器官和病变的顺序或关节分割实施，这需要对器官分割进行其他监督。取而代之的是，我们建议以无其他标记成本的靶病变的强度分布来有效地分开病变位于背景的区域。它被整合到网络培训中，作为一项辅助任务。我们将提出的方法应用于CT扫描中小肠癌小肿瘤的分割。我们观察到所有指标的改进（33.5％$ \ rightarrow $ 38.2％，41.3％$ \ rightarrow $ 47.8％，30.0％$ \ rightarrow $ \ rightArrow $ 35.9％的全球，每个案例和每个肿瘤角骰子得分分别为35.9％。我们的方法可以是在网络培训中明确合并目标的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% $\rightarrow$ 38.2%, 41.3% $\rightarrow$ 47.8%, 30.0% $\rightarrow$ 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.
