Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration

2021/11/15 by Wenhao Li, Qisen Xu, Li, Wenhao +13
Computer Science · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Radiomics and Machine Learning in Medical Imaging

paper · pdf · doi:10.48550/arxiv.2111.07716

openalex publication_date 2021/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
• A Practical Bayesian Framework for Backpropagation Networks
• Learning Confidence for Out-of-Distribution Detection in Neural Networks
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms
• Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
• Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
• PyTorch: An Imperative Style, High-Performance Deep Learning Library

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• CAD: Confidence-Aware Adaptive Displacement for Semi-Supervised Medical Image Segmentation
• Learning from Disagreement: A Group Decision Simulation Framework for Robust Medical Image Segmentation