Pre-trained segmentation models are a powerful and flexible tool for segmenting images. Recently, this trend has extended to medical imaging. Yet, often these methods only produce a single prediction for a given image, neglecting inherent uncertainty in medical images, due to unclear object boundaries and errors caused by the annotation tool. Multiple Choice Learning is a technique for generating multiple masks, through multiple learned prediction heads. However, this cannot readily be extended to producing more outputs than its initial pre-training hyperparameters, as the sparse, winner-takes-all loss function makes it easy for one prediction head to become overly dominant, thus not guaranteeing the clinical relevancy of each mask produced. We introduce SeqSAM, a sequential, RNN-inspired approach to generating multiple masks, which uses a bipartite matching loss for ensuring the clinical relevancy of each mask, and can produce an arbitrary number of masks. We show notable improvements in quality of each mask produced across two publicly available datasets. Our code is available at https://github.com/BenjaminTowle/SeqSAM.

本研究解决了医学图像分割方法仅生成单一预测而忽视不确定性的问题。提出的SeqSAM方法采用序列化、RNN启发的策略，通过二部匹配损失来保证每个分割掩模的临床相关性，并能够生成任意数量的掩模。研究显示，该方法在两个公开数据集上显著提高了分割掩模的质量。

SeqSAM：基于SAM的医学图像分割自回归多假设预测