3D Universal Lesion Detection and Tagging in CT with Self-Training
Journal:
arXiv
Published Date:
Apr 7, 2025
Abstract
Radiologists routinely perform the tedious task of lesion localization,
classification, and size measurement in computed tomography (CT) studies.
Universal lesion detection and tagging (ULDT) can simultaneously help alleviate
the cumbersome nature of lesion measurement and enable tumor burden assessment.
Previous ULDT approaches utilize the publicly available DeepLesion dataset,
however it does not provide the full volumetric (3D) extent of lesions and also
displays a severe class imbalance. In this work, we propose a self-training
pipeline to detect 3D lesions and tag them according to the body part they
occur in. We used a significantly limited 30\% subset of DeepLesion to train a
VFNet model for 2D lesion detection and tagging. Next, the 2D lesion context
was expanded into 3D, and the mined 3D lesion proposals were integrated back
into the baseline training data in order to retrain the model over multiple
rounds. Through the self-training procedure, our VFNet model learned from its
own predictions, detected lesions in 3D, and tagged them. Our results indicated
that our VFNet model achieved an average sensitivity of 46.9\% at [0.125:8]
false positives (FP) with a limited 30\% data subset in comparison to the
46.8\% of an existing approach that used the entire DeepLesion dataset. To our
knowledge, we are the first to jointly detect lesions in 3D and tag them
according to the body part label.
