计算机科学
人工智能
分割
编码器
一致性(知识库)
边界(拓扑)
编码(集合论)
解码方法
GSM演进的增强数据速率
计算机视觉
图像(数学)
像素
模式识别(心理学)
图像分割
算法
数学
数学分析
集合(抽象数据类型)
程序设计语言
操作系统
作者
Lei Li,Sheng Lian,Zhiming Luo,Beizhan Wang,Shaozi Li
标识
DOI:10.1016/j.bspc.2023.105694
摘要
In medical images, the edges of organs are often blurred and unclear. Existing semi-supervised image segmentation methods rarely model edges explicitly. Thus most methods produce inaccurate predictions in target edge regions. In this paper, we propose a contour-aware consistency framework for semi-supervised medical image segmentation. The framework consists of a shared encoder, a vanilla primary decoder and a contour-enhanced auxiliary decoder. The contour-enhanced decoder is designed to enhance the features of the target contour region. The predictions from the primary decoder and the auxiliary decoder are combined to create pseudo labels, enabling the unlabeled data for supervision. For the inconsistent regions in predictions, we propose a self-contrast strategy that further improves the performance by reducing the discrepancy of the dual decoder for the same pixel. We conducted extensive experiments on three publicly available datasets and verified that our approach outperforms other methods for boundary quality. Specifically, with 5% labeled data on Left Atrial (LA) dataset, our proposed approach achieved a Boundary IoU 3.76% higher than the state-of-the-art methods. Code is available at https://github.com/SmileJET/CAC4SSL.
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