Weakly-Supervised Detection of Bone Lesions in CT
Tao Sheng Tejas Sudharshan Mathai Alexander Shieh Ronald M. Summers
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.