LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging
Journal:
arXiv
Published Date:
Feb 28, 2025
Abstract
In this work, we present LesionLocator, a framework for zero-shot
longitudinal lesion tracking and segmentation in 3D medical imaging,
establishing the first end-to-end model capable of 4D tracking with dense
spatial prompts. Our model leverages an extensive dataset of 23,262 annotated
medical scans, as well as synthesized longitudinal data across diverse lesion
types. The diversity and scale of our dataset significantly enhances model
generalizability to real-world medical imaging challenges and addresses key
limitations in longitudinal data availability. LesionLocator outperforms all
existing promptable models in lesion segmentation by nearly 10 dice points,
reaching human-level performance, and achieves state-of-the-art results in
lesion tracking, with superior lesion retrieval and segmentation accuracy.
LesionLocator not only sets a new benchmark in universal promptable lesion
segmentation and automated longitudinal lesion tracking but also provides the
first open-access solution of its kind, releasing our synthetic 4D dataset and
model to the community, empowering future advancements in medical imaging. Code
is available at: www.github.com/MIC-DKFZ/LesionLocator
