TCCNet: Temporally Consistent Context-Free Network for Semi-supervised Video Polyp Segmentation
Xiaotong Li, Jilan Xu, Yuejie Zhang, Rui Feng, Rui-Wei Zhao, Tao Zhang, Xuequan Lu, Shang Gao
Abstract
Automatic video polyp segmentation (VPS) is highly valued for the early diagnosis of colorectal cancer. However, existing methods are limited in three respects: 1) most of them work on static images, while ignoring the temporal information in consecutive video frames; 2) all of them are fully supervised and easily overfit in presence of limited annotations; 3) the context of polyp (i.e., lumen, specularity and mucosa tissue) varies in an endoscopic clip, which may affect the predictions of adjacent frames. To resolve these challenges, we propose a novel Temporally Consistent Context-Free Network (TCCNet) for semi-supervised VPS. It contains a segmentation branch and a propagation branch with a co-training scheme to supervise the predictions of unlabeled image. To maintain the temporal consistency of predictions, we design a Sequence-Corrected Reverse Attention module and a Propagation-Corrected Reverse Attention module. A Context-Free Loss is also proposed to mitigate the impact of varying contexts. Extensive experiments show that even trained under 1/15 label ratio, TCCNet is comparable to the state-of-the-art fully supervised methods for VPS. Also, TCCNet surpasses existing semi-supervised methods for natural image and other medical image segmentation tasks.