We will develop a 3D slicer extension to deploy two interactive segmentation models aimed at helping researchers and clinicians perform new segmentation tasks:
ScribblePrompt is a deep learning model that enables users to interactively segment an image using scribbles, clicks, and bounding boxes. The model is designed to generalize to new labels and types of biomedical images and uses a lightweight UNet-based architecture so it runs quickly even without a GPU.
MultiverSeg extends this interactive approach to speed up the segmentation of sets of similar images. Using the same interaction types as ScribblePrompt (scribbles, clicks, bounding boxes), the system learns from each segmentation to improve subsequent predictions. Given enough similar example segmentations, MultiverSeg can also automatically segment new images without any user interaction.
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Relevant Publications:
Wong, H.E., Rakic, M., Guttag, J., & Dalca, A.V., (2024). ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image. In European Conference on Computer Vision. paper code
Wong, H.E., Ortiz, J.J.G., Guttag, J. & Dalca, A.V., (2024). MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance. arXiv preprint arXiv:2412.15058. paper code
Related 3D Slicer extensions: