Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration

• URL: http://arxiv.org/abs/2111.07716v1
• Date: Mon, 15 Nov 2021 12:38:56 GMT
• Title: Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration
• Authors: Wenhao Li and Qisen Xu and Chuyun Shen and Bin Hu and Fengping Zhu and Yuxin Li and Bo Jin and Xiangfeng Wang
• Abstract summary: This paper proposes an interactive segmentation framework, called interactive MEdical segmentation with self-adaptive Confidence CAlibration (MECCA) The evaluation is established through a novel action-based confidence network, and the corrective actions are obtained from MARL. Experimental results on various medical image datasets have shown the significant performance of the proposed algorithm.
• Score: 10.297081695050457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation is one of the fundamental problems for artificial intelligence-based clinical decision systems. Current automatic medical image segmentation methods are often failed to meet clinical requirements. As such, a series of interactive segmentation algorithms are proposed to utilize expert correction information. However, existing methods suffer from some segmentation refining failure problems after long-term interactions and some cost problems from expert annotation, which hinder clinical applications. This paper proposes an interactive segmentation framework, called interactive MEdical segmentation with self-adaptive Confidence CAlibration (MECCA), by introducing the corrective action evaluation, which combines the action-based confidence learning and multi-agent reinforcement learning (MARL). The evaluation is established through a novel action-based confidence network, and the corrective actions are obtained from MARL. Based on the confidential information, a self-adaptive reward function is designed to provide more detailed feedback, and a simulated label generation mechanism is proposed on unsupervised data to reduce over-reliance on labeled data. Experimental results on various medical image datasets have shown the significant performance of the proposed algorithm.

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