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.

翻译：群体专家的集体智慧在临床任务中始终优于个体最佳诊断。针对医学图像分割任务，现有基于人工智能的替代研究更多关注开发能模仿最佳个体的模型，而非利用专家群体的力量。本文提出了一种基于单一扩散模型的方法，通过学习群体见解的分布来生成多个合理输出。该模型仅需极少的额外学习，即可利用扩散固有的随机采样过程生成分割掩码分布。我们在CT、超声和MRI三种不同医学影像模态上验证了模型能够生成多种变异结果并捕捉其出现频率。综合结果表明，本方法在保持自然变异的同时，在准确性上超越了现有最先进的模糊分割网络。我们还提出了一项新指标，用于评估分割预测的多样性与准确性，该指标与临床实践中集体见解的应用需求相契合。