Dynamically evolving segment anything model with continuous learning for medical image segmentation
Journal:
arXiv
Published Date:
Mar 8, 2025
Abstract
Medical image segmentation is essential for clinical diagnosis, surgical
planning, and treatment monitoring. Traditional approaches typically strive to
tackle all medical image segmentation scenarios via one-time learning. However,
in practical applications, the diversity of scenarios and tasks in medical
image segmentation continues to expand, necessitating models that can
dynamically evolve to meet the demands of various segmentation tasks. Here, we
introduce EvoSAM, a dynamically evolving medical image segmentation model that
continuously accumulates new knowledge from an ever-expanding array of
scenarios and tasks, enhancing its segmentation capabilities. Extensive
evaluations on surgical image blood vessel segmentation and multi-site prostate
MRI segmentation demonstrate that EvoSAM not only improves segmentation
accuracy but also mitigates catastrophic forgetting. Further experiments
conducted by surgical clinicians on blood vessel segmentation confirm that
EvoSAM enhances segmentation efficiency based on user prompts, highlighting its
potential as a promising tool for clinical applications.
