Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration
This work addresses the challenge of refining segmentation failures and reducing annotation costs in clinical applications, representing an incremental advancement in interactive medical image segmentation.
The paper tackled the problem of interactive medical image segmentation by proposing MECCA, a framework that integrates corrective action evaluation with multi-agent reinforcement learning and self-adaptive confidence calibration, resulting in significant performance improvements on various datasets.
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.