无需标注数据，用交互引导快速分割新医学图像集
MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance
- 通过用户点击/框选/涂画提供初始输入，随标注增多自动积累上下文
- 在未见任务上实现90% Dice时，点击数减少36%，涂画步骤减少25%
- 适合无标注数据的医学图像新任务快速分割，降低人工成本
医学研究者常需对一组相关图像执行新分割任务。现有方法要么需大量人工逐图操作，要么依赖已有标注数据。我们提出MultiverSeg系统，可在无需任何该任务或领域已有标注的情况下，快速分割整个新数据集。模型接收待分割图像及用户交互（如点击、框选、涂画）作为输入，随着用户标注图像增多，这些标注数据作为上下文输入模型，使后续图像所需交互逐渐减少。实验表明，相比先进交互分割方法，MultiverSeg在未见任务图像集上实现90% Dice分数时，总点击数减少36%，涂画步骤减少25%。代码与模型权重已公开于https://multiverseg.csail.mit.edu
原文摘要 · Abstract (English)
Medical researchers and clinicians often need to perform novel segmentation tasks on a set of related images. Existing methods for segmenting a new dataset are either interactive, requiring substantial human effort for each image, or require an existing set of previously labeled images. We introduce a system, MultiverSeg, that enables practitioners to rapidly segment an entire new dataset without requiring access to any existing labeled data from that task or domain. Along with the image to segment, the model takes user interactions such as clicks, bounding boxes or scribbles as input, and predicts a segmentation. As the user segments more images, those images and segmentations become additional inputs to the model, providing context. As the context set of labeled images grows, the number of interactions required to segment each new image decreases. We demonstrate that MultiverSeg enables users to interactively segment new datasets efficiently, by amortizing the number of interactions per image to achieve an accurate segmentation. Compared to using a state-of-the-art interactive segmentation method, MultiverSeg reduced the total number of clicks by 36% and scribble steps by 25% to achieve 90% Dice on sets of images from unseen tasks. We release code and model weights at https://multiverseg.csail.mit.edu
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