Enforcing Mutual Consistency of Hard Regions for Semi-supervised Medical Image Segmentation

• URL: http://arxiv.org/abs/2109.09960v1
• Date: Tue, 21 Sep 2021 04:47:42 GMT
• Title: Enforcing Mutual Consistency of Hard Regions for Semi-supervised Medical Image Segmentation
• Authors: Yicheng Wu, Zongyuan Ge, Donghao Zhang, Minfeng Xu, Lei Zhang, Yong Xia and Jianfei Cai
• Abstract summary: We propose a novel mutual consistency network (MC-Net+) to exploit the unlabeled hard regions for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions. We compare the segmentation results of the MC-Net+ with five state-of-the-art semi-supervised approaches on three public medical datasets.
• Score: 68.9233942579956
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we proposed a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled hard regions for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions in the ambiguous regions (e.g. adhesive edges or thin branches) for the image segmentation task. Leveraging these region-level challenging samples can make the semi-supervised segmentation model training more effective. Therefore, our proposed MC-Net+ model consists of two new designs. First, the model contains one shared encoder and multiple sightly different decoders (i.e. using different up-sampling strategies). The statistical discrepancy of multiple decoders' outputs is computed to denote the model's uncertainty, which indicates the unlabeled hard regions. Second, a new mutual consistency constraint is enforced between one decoder's probability output and other decoders' soft pseudo labels. In this way, we minimize the model's uncertainty during training and force the model to generate invariant and low-entropy results in such challenging areas of unlabeled data, in order to learn a generalized feature representation. We compared the segmentation results of the MC-Net+ with five state-of-the-art semi-supervised approaches on three public medical datasets. Extension experiments with two common semi-supervised settings demonstrate the superior performance of our model over other existing methods, which sets a new state of the art for semi-supervised medical image segmentation.

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