2026-09-19T12:18:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1561172024-08-15T03:52:43Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Guttag, John V.
Dalca, Adrian V.
Butoi, Victor Ion
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-08-14T19:52:44Z
2024-08-14T19:52:44Z
2024-05
2024-07-10T12:59:28.758Z
https://hdl.handle.net/1721.1/156117
0000-0001-7118-1492
While deep learning models have become the predominant method for medical image segmentation, they are typically incapable of generalizing to new segmentation tasks---involving new anatomies, image modalities, or labels. For a new segmentation task, researchers will often have to prepare new task-specific models. This process is time-consuming and poses a substantial barrier for clinical researchers who often lack the resources and expertise to train neural networks.
We present UniverSeg, an in-context learning method for solving unseen medical segmentation tasks. Given a new image to segment, and a set of image-label pairs that define the task, UniverSeg can produce accurate segmentation predictions with no additional training. We demonstrate that UniverSeg substantially outperforms existing methods in solving unseen segmentation tasks, and thoroughly analyze important aspects of our proposed data, training, and inference paradigms.
S.M.
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
Learning to Segment Unseen Tasks In-Context
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>While deep learning models have become the predominant method for medical image segmentation, they are typically incapable of generalizing to new segmentation tasks---involving new anatomies, image modalities, or labels. For a new segmentation task, researchers will often have to prepare new task-specific models. This process is time-consuming and poses a substantial barrier for clinical researchers who often lack the resources and expertise to train neural networks.
We present UniverSeg, an in-context learning method for solving unseen medical segmentation tasks. Given a new image to segment, and a set of image-label pairs that define the task, UniverSeg can produce accurate segmentation predictions with no additional training. We demonstrate that UniverSeg substantially outperforms existing methods in solving unseen segmentation tasks, and thoroughly analyze important aspects of our proposed data, training, and inference paradigms.</Abstract>
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