准确的肠道分割对肠癌的诊断和治疗至关重要。不幸的是，在 CT 图像中分割整个肠道是相当具有挑战性的，因为肠道边界不清晰、形状大大小小、种类繁多、填充状态不同。在本文中，我们提出了一种名为 BowelNet 的新型两阶段框架来处理 CT 图像中具有挑战性的肠道分割任务，分别为：1）共同定位所有类型的肠道，以及 2）精细分割每种类型的肠道。具体而言，在第一阶段，我们从部分标注和完全标注的 CT 图像中学习了一个统一的定位网络来强化检测所有类型的肠道。为了更好地捕捉不清晰的肠道边界和学习复杂的肠道形状，在第二阶段，我们提出采用多任务学习方案联合学习语义信息（即肠道分割掩码）和几何表示（即肠道边界和肠道骨架）进行精细的肠道分割。此外，我们还进一步提出通过伪标签学习元分割网络，以提高分割准确性。通过在大型腹部 CT 数据集上评估，我们提出的 BowelNet 方法在分割十二指肠、空肠回肠、结肠、乙状结肠和直肠方面达到了 Dice 得分分别为 0.764、0.848、0.835、0.774 和 0.824。这些结果表明了我们提出的 BowelNet 框架在从 CT 图像中分割整个肠道方面的有效性。

Accurate bowel segmentation is essential for diagnosis and treatment of bowel cancers. Unfortunately, segmenting the entire bowel in CT images is quite challenging due to unclear boundary, large shape, size, and appearance variations, as well as diverse filling status within the bowel. In this paper, we present a novel two-stage framework, named BowelNet, to handle the challenging task of bowel segmentation in CT images, with two stages of 1) jointly localizing all types of the bowel, and 2) finely segmenting each type of the bowel. Specifically, in the first stage, we learn a unified localization network from both partially- and fully-labeled CT images to robustly detect all types of the bowel. To better capture unclear bowel boundary and learn complex bowel shapes, in the second stage, we propose to jointly learn semantic information (i.e., bowel segmentation mask) and geometric representations (i.e., bowel boundary and bowel skeleton) for fine bowel segmentation in a multi-task learning scheme. Moreover, we further propose to learn a meta segmentation network via pseudo labels to improve segmentation accuracy. By evaluating on a large abdominal CT dataset, our proposed BowelNet method can achieve Dice scores of 0.764, 0.848, 0.835, 0.774, and 0.824 in segmenting the duodenum, jejunum-ileum, colon, sigmoid, and rectum, respectively. These results demonstrate the effectiveness of our proposed BowelNet framework in segmenting the entire bowel from CT images.