Ambiguous Medical Image Segmentation using Diffusion Models
- URL: http://arxiv.org/abs/2304.04745v1
- Date: Mon, 10 Apr 2023 17:58:22 GMT
- Title: Ambiguous Medical Image Segmentation using Diffusion Models
- Authors: Aimon Rahman and Jeya Maria Jose Valanarasu and Ilker Hacihaliloglu
and Vishal M Patel
- Abstract summary: 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 sampling process of diffusion.
Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
- Score: 60.378180265885945
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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