Weakly-supervised detection of bone lesions in CT

Tao Sheng; Tejas Sudharshan Mathai; Alexander Shieh; Ronald M. Summers

doi:10.1117/12.3008823

3 April 2024 Weakly-supervised detection of bone lesions in CT

Tao Sheng, Tejas Sudharshan Mathai, Alexander Shieh, Ronald M. Summers

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129270Q (2024) https://doi.org/10.1117/12.3008823
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. CT is routinely used to measure the size of lesions in the bones. However, they can be difficult to spot due to the wide variations in their sizes, shapes, and appearances. Precise localization of such lesions would enable reliable tracking of interval changes (growth, shrinkage, or unchanged status). To that end, an automated technique to detect bone lesions is highly desirable. In this pilot work, we developed a pipeline to detect bone lesions (lytic, blastic, and mixed) in CT volumes via a proxy segmentation task. First, we used the bone lesions that were prospectively marked by radiologists in a few 2D slices of CT volumes and converted them into weak 3D segmentation masks. Then, we trained a 3D full-resolution nnUNet model using these weak 3D annotations to segment the lesions and thereby detected them. Our automated method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data. To the best of our knowledge, we are the first to attempt the direct detection of bone lesions in CT via a proxy segmentation task.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tao Sheng, Tejas Sudharshan Mathai, Alexander Shieh, and Ronald M. Summers "Weakly-supervised detection of bone lesions in CT", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129270Q (3 April 2024); https://doi.org/10.1117/12.3008823

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