Diffusion-Based Quality Control of Medical Image Segmentations across Organs

TL;DR

This paper introduces nnQC, a diffusion-based quality control framework for medical image segmentation that self-adapts to various organs and datasets, outperforming existing organ-specific methods.

Contribution

The paper presents a novel diffusion-generative QC method with a Team of Experts architecture that adapts across organs, datasets, and modalities, improving segmentation quality assessment.

Findings

nnQC outperforms state-of-the-art QC methods across seven organs.

It remains effective even with highly degraded or missing segmentation masks.

The framework demonstrates versatility across different datasets and imaging modalities.

Abstract

Medical image segmentation using deep learning (DL) has enabled the development of automated analysis pipelines for large-scale population studies. However, state-of-the-art DL methods are prone to hallucinations, which can result in anatomically implausible segmentations. With manual correction impractical at scale, automated quality control (QC) techniques have to address the challenge. While promising, existing QC methods are organ-specific, limiting their generalizability and usability beyond their original intended task. To overcome this limitation, we propose no-new Quality Control (nnQC), a robust QC framework based on a diffusion-generative paradigm that self-adapts to any input organ dataset. Central to nnQC is a novel Team of Experts (ToE) architecture, where two specialized experts independently encode 3D spatial awareness, represented by the relative spatial position of an axial slice, and anatomical information derived from visual features from the original image. A weighted conditional module dynamically combines the pair of independent embeddings, or opinions to condition the sampling mechanism within a diffusion process, enabling the generation of a spatially aware pseudo-ground truth for predicting QC scores. Within its framework, nnQC integrates fingerprint adaptation to ensure adaptability across organs, datasets, and imaging modalities. We evaluated nnQC on seven organs using twelve publicly available datasets. Our results demonstrate that nnQC consistently outperforms state-of-the-art methods across all experiments, including cases where segmentation masks are highly degraded or completely missing, confirming its versatility and effectiveness across different organs.