RefineSeg: Dual Coarse-to-Fine Learning for Medical Image Segmentation
This addresses the annotation bottleneck for medical image segmentation, offering a practical solution to reduce reliance on expert-labeled data, though it is incremental as it builds on weakly supervised methods.
The paper tackles the problem of costly pixel-level annotations for medical image segmentation by proposing a coarse-to-fine framework that uses only noisy coarse annotations, achieving results that surpass state-of-the-art weakly supervised methods and closely match fully supervised approaches on cardiac imaging datasets.
High-quality pixel-level annotations of medical images are essential for supervised segmentation tasks, but obtaining such annotations is costly and requires medical expertise. To address this challenge, we propose a novel coarse-to-fine segmentation framework that relies entirely on coarse-level annotations, encompassing both target and complementary drawings, despite their inherent noise. The framework works by introducing transition matrices in order to model the inaccurate and incomplete regions in the coarse annotations. By jointly training on multiple sets of coarse annotations, it progressively refines the network's outputs and infers the true segmentation distribution, achieving a robust approximation of precise labels through matrix-based modeling. To validate the flexibility and effectiveness of the proposed method, we demonstrate the results on two public cardiac imaging datasets, ACDC and MSCMRseg, and further evaluate its performance on the UK Biobank dataset. Experimental results indicate that our approach surpasses the state-of-the-art weakly supervised methods and closely matches the fully supervised approach.