Scribble-based 3D Multiple Abdominal Organ Segmentation via
  Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency
Meng Han Xiangde Luo Wenjun Liao Shichuan Zhang Shaoting Zhang Guotai Wang
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.