Medical professionals, including clinicians and scientists, frequently encounter new segmentation tasks and protocols for different anatomies, necessitating the training of additional or fine-tuned machine learning networks. Unfortunately, this process can be time-consuming and inaccessible to many due to the technical expertise and hardware required. “This kind of problem necessitates that you have a model that is flexible to new tasks, without retraining, that can be deployed at inference,” Victor begins. “Our model, UniverSeg, can segment new tasks without retraining across a variety of different anatomies. It takes advantage of context learning, a mechanism that hasn’t been well explored in medical segmentation thus far. It has a place as a foundational model for different applications.” This work is not about enabling more tasks but rather simplifying the adoption of machine-learning tools for medical segmentation. When a clinician encounters a new problem and collects medical scans, it can take months to collaborate with an expert to train and deploy a model. UniverSeg streamlines this process by eliminating the need for training. 8 DAILY ICCV Friday Poster Presentation UniverSeg: Universal Medical Image Segmentation Victor Ion Butoi (right) is a second-year PhD student, and Jose Javier Gonzalez Ortiz (left) is a final-year PhD student at MIT, supervised by Adrian Dalca. They speak to us ahead of their poster this afternoon on UniverSeg, a novel method for solving unseen medical segmentation tasks without additional training.
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