Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Foundation Model, Lesion Tracking, Longitudinal data
Motivation: Automate lesion(s) delineation across longitudinal time-points to improve throughput and accuracy, reduce fatigue and determine disease velocity
Goal(s): A method where user identifies an imaging lesion and wants to automatically label phenotypically similar imaging lesion on other scans .
Approach: Vision Foundation model (DINO V2) features to localize and segment similar region between template mask region and new test data to obtain segmentation of similar lesion(s).
Results: Reasonably well lesion segmentation capabilities on serial MRI scans in oncology patients with various MRI protocols, orientations and contrast. For Ferret diameter metric, a mean difference (95% CI) = -3.5 mm (-7.6 to 0.7 mm).
Impact: Ability to automatically delineate phenotypically similar lesions on serial imaging data with user interaction on first time point only. Methodology is generalizable irrespective of imaging orientation, contrast and without need for extensive data labelling or geometric synchronization on serial scans.
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