Ambiguous Medical Image Segmentation using Diffusion Models
This addresses the need for AI models that capture collective expert insights in medical image segmentation, offering a more realistic alternative to single-output models for clinical practice.
The paper tackles the problem of generating multiple plausible segmentation masks for ambiguous medical images by introducing a diffusion model that learns a distribution over expert group insights, outperforming existing state-of-the-art ambiguous segmentation networks in accuracy while preserving natural variation.
Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights.