Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction
Journal:
arXiv
Published Date:
Jan 26, 2025
Abstract
Metal artifacts in computed tomography (CT) images can significantly degrade
image quality and impede accurate diagnosis. Supervised metal artifact
reduction (MAR) methods, trained using simulated datasets, often struggle to
perform well on real clinical CT images due to a substantial domain gap.
Although state-of-the-art semi-supervised methods use pseudo ground-truths
generated by a prior network to mitigate this issue, their reliance on a fixed
prior limits both the quality and quantity of these pseudo ground-truths,
introducing confirmation bias and reducing clinical applicability. To address
these limitations, we propose a novel Radiologist-In-the-loop SElf-training
framework for MAR, termed RISE-MAR, which can integrate radiologists' feedback
into the semi-supervised learning process, progressively improving the quality
and quantity of pseudo ground-truths for enhanced generalization on real
clinical CT images. For quality assurance, we introduce a clinical quality
assessor model that emulates radiologist evaluations, effectively selecting
high-quality pseudo ground-truths for semi-supervised training. For quantity
assurance, our self-training framework iteratively generates additional
high-quality pseudo ground-truths, expanding the clinical dataset and further
improving model generalization. Extensive experimental results on multiple
clinical datasets demonstrate the superior generalization performance of our
RISE-MAR over state-of-the-art methods, advancing the development of MAR models
for practical application. Code is available at
https://github.com/Masaaki-75/rise-mar.
