# Weakly-Supervised Detection of Bone Lesions in CT

arXiv: https://arxiv.org/abs/2402.00175 · HTML: https://mlsanity.com/paper/2402.00175/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels](https://mlsanity.com/paper/1906.02283/) (2019)
- [Universal Lesion Detection in CT Scans using Neural Network Ensembles](https://mlsanity.com/paper/2111.04886/) (2021)
- [Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications](https://mlsanity.com/paper/1407.5976/) (2014)

## Built on by (most cited first, 3 of 3)

- [Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT](https://mlsanity.com/paper/2504.06921/) (2025)
- [Text Embedded Swin-UMamba for DeepLesion Segmentation](https://mlsanity.com/paper/2508.06453/) (2025)
- [Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT](https://mlsanity.com/paper/2512.06849/) (2025)

## Abstract (arXiv)

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.