Inconsistency-Aware Uncertainty Estimation for Semi-Supervised Medical Image Segmentation

分割 人工智能 计算机科学 尺度空间分割 图像分割 像素 基于分割的对象分类 模式识别(心理学) 水准点(测量) 熵(时间箭头) 计算机视觉 物理 量子力学 大地测量学 地理
作者
Yinghuan Shi,Jian Zhang,Tong Ling,Jiwen Lu,Yefeng Zheng,Qian Yu,Lei Qi,Yang Gao
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:41 (3): 608-620 被引量:126
标识
DOI:10.1109/tmi.2021.3117888
摘要

In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conservative-radical network ( CoraNet in short) based on our uncertainty estimation and separate self-training strategy. In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN) that could be alternatively trained in an end-to-end manner. We have extensively evaluated our method on various segmentation tasks with publicly available benchmark datasets, including CT pancreas, MR endocardium, and MR multi-structures segmentation on the ACDC dataset. Compared with the current state of the art, our CoraNet has demonstrated superior performance. In addition, we have also analyzed its connection with and difference from conventional methods of uncertainty estimation in semi-supervised medical image segmentation.
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