Multi-organ segmentation in abdominal Computed Tomography (CT) images is of
great importance for diagnosis of abdominal lesions and subsequent treatment
planning. Though deep learning based methods have attained high performance,
they rely heavily on large-scale pixel-level annotations that are
time-consuming and labor-intensive to obtain. Due to its low dependency on
annotation, weakly supervised segmentation has attracted great attention.
However, there is still a large performance gap between current
weakly-supervised methods and fully supervised learning, leaving room for
exploration. In this work, we propose a novel 3D framework with two consistency
constraints for scribble-supervised multiple abdominal organ segmentation from
CT. Specifically, we employ a Triple-branch multi-Dilated network (TDNet) with
one encoder and three decoders using different dilation rates to capture
features from different receptive fields that are complementary to each other
to generate high-quality soft pseudo labels. For more stable unsupervised
learning, we use voxel-wise uncertainty to rectify the soft pseudo labels and
then supervise the outputs of each decoder. To further regularize the network,
class relationship information is exploited by encouraging the generated class
affinity matrices to be consistent across different decoders under multi-view
projection. Experiments on the public WORD dataset show that our method
outperforms five existing scribble-supervised methods.

基于 CT 图像的腹部多器官分割是诊断腹部病变和后续治疗规划至关重要的一项任务。本研究提出了一种新颖的 3D 框架，通过两种一致性约束方法进行 scribble 辅助的多腹部器官分割，表明该方法优于现有的五种 scribble 辅助方法。