MedUHIP: Towards Human-In-the-Loop Medical Segmentation

TL;DR

MedUHIP introduces a human-in-the-loop, uncertainty-aware medical image segmentation method that combines automated plausible segmentation proposals with clinician refinement, improving accuracy and safety in uncertain medical images.

Contribution

This work presents a novel collaborative approach integrating uncertainty-aware modeling with clinician interaction, advancing medical segmentation accuracy and efficiency.

Findings

Outperforms existing deterministic and uncertainty-aware models.

Requires fewer clinician interactions for high-quality segmentation.

Effective on multiple publicly available datasets.

Abstract

Although segmenting natural images has shown impressive performance, these techniques cannot be directly applied to medical image segmentation. Medical image segmentation is particularly complicated by inherent uncertainties. For instance, the ambiguous boundaries of tissues can lead to diverse but plausible annotations from different clinicians. These uncertainties cause significant discrepancies in clinical interpretations and impact subsequent medical interventions. Therefore, achieving quantitative segmentations from uncertain medical images becomes crucial in clinical practice. To address this, we propose a novel approach that integrates an \textbf{uncertainty-aware model} with \textbf{human-in-the-loop interaction}. The uncertainty-aware model proposes several plausible segmentations to address the uncertainties inherent in medical images, while the human-in-the-loop interaction iteratively modifies the segmentation under clinician supervision. This collaborative model ensures that segmentation is not solely dependent on automated techniques but is also refined through clinician expertise. As a result, our approach represents a significant advancement in the field which enhances the safety of medical image segmentation. It not only offers a comprehensive solution to produce quantitative segmentation from inherent uncertain medical images, but also establishes a synergistic balance between algorithmic precision and clincian knowledge. We evaluated our method on various publicly available multi-clinician annotated datasets: REFUGE2, LIDC-IDRI and QUBIQ. Our method showcases superior segmentation capabilities, outperforming a wide range of deterministic and uncertainty-aware models. We also demonstrated that our model produced significantly better results with fewer interactions compared to previous interactive models. We will release the code to foster further research in this area.