UniverSeg: Universal Medical Image Segmentation
- URL: http://arxiv.org/abs/2304.06131v1
- Date: Wed, 12 Apr 2023 19:36:46 GMT
- Title: UniverSeg: Universal Medical Image Segmentation
- Authors: Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R.
Sabuncu, John Guttag, Adrian V. Dalca
- Abstract summary: We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training.
We have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans.
We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks.
- Score: 16.19510845046103
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While deep learning models have become the predominant method for medical
image segmentation, they are typically not capable of generalizing to unseen
segmentation tasks involving new anatomies, image modalities, or labels. Given
a new segmentation task, researchers generally have to train or fine-tune
models, which 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, a method for solving unseen medical
segmentation tasks without additional training. Given a query image and example
set of image-label pairs that define a new segmentation task, UniverSeg employs
a new Cross-Block mechanism to produce accurate segmentation maps without the
need for additional training. To achieve generalization to new tasks, we have
gathered and standardized a collection of 53 open-access medical segmentation
datasets with over 22,000 scans, which we refer to as MegaMedical. We used this
collection to train UniverSeg on a diverse set of anatomies and imaging
modalities. We demonstrate that UniverSeg substantially outperforms several
related methods on unseen tasks, and thoroughly analyze and draw insights about
important aspects of the proposed system. The UniverSeg source code and model
weights are freely available at https://universeg.csail.mit.edu
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