Body Part Regression for CT Images

TL;DR

This paper introduces a self-supervised body part regression model for CT images that enhances the transferability of deep learning models into clinical practice by accurately recognizing body regions.

Contribution

It presents a novel self-supervised approach for fine-grained body part recognition in CT volumes and integrates it into a medical platform for practical use.

Findings

Improves robustness of body part recognition in CT images

Facilitates reliable transfer of deep learning models to clinical settings

Provides an easy-to-use Python package for the medical community

Abstract

One of the greatest challenges in the medical imaging domain is to successfully transfer deep learning models into clinical practice. Since models are often trained on a specific body region, a robust transfer into the clinic necessitates the selection of images with body regions that fit the algorithm to avoid false-positive predictions in unknown regions. Due to the insufficient and inaccurate nature of manually-defined imaging meta-data, automated body part recognition is a key ingredient towards the broad and reliable adoption of medical deep learning models. While some approaches to this task have been presented in the past, building and evaluating robust algorithms for fine-grained body part recognition remains challenging. So far, no easy-to-use method exists to determine the scanned body range of medical Computed Tomography (CT) volumes. In this thesis, a self-supervised body part regression model for CT volumes is developed and trained on a heterogeneous collection of CT studies. Furthermore, it is demonstrated how the algorithm can contribute to the robust and reliable transfer of medical models into the clinic. Finally, easy application of the developed method is ensured by integrating it into the medical platform toolkit Kaapana and providing it as a python package at https://github.com/MIC-DKFZ/BodyPartRegression .