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.

该研究提出了一种利用扩散模型和随机采样方法生成多个可行的医学图像分割结果的方法，这种方法应用于 CT、超声波和 MRI 图像时，不仅能够提高分割准确度而且能够捕捉自然变异，同时还提出了一种度量分割结果多样性的新指标。