Segmentation and analysis of lumbar spine mri scans for vertebral body measurements
This paper investigates a data- and knowledge-driven approach to automatically analyze lumbar MRI scans. The dataset used is an in-house dataset of 142 sagital lumbar spine images from German radiology practices of the evidia GmbH. We implement state-of-the-art deep learning methods to segment the individual vertebral bodies. Overall, a very accurate segmentation performance of 97% Dice Score was achieved. Based on this segmentation, pathologically relevant distances are calculated using rule-based computer vision methods. We focus on the anterior, posterior and middle height of a vertebra and the anterior and posterior distances between two lumbar vertebrae. We demonstrate the clinical value of this approach through a quantitative and qualitative result analysis.
- Published in:
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Type:
Inproceedings - Authors:
Schneider, Helen; Biesner, David; Ashokan, Akash; Broß, Maximilian; Kador, Rebecca; Bagyo, Gabor; Dankerl, Peter; Ragab, Haissam; Yamamura, Jin; Halscheidt, Sandra; Labisch, Christoph; Sifa, Rafet - Year:
2023
Citation information
Schneider, Helen; Biesner, David; Ashokan, Akash; Broß, Maximilian; Kador, Rebecca; Bagyo, Gabor; Dankerl, Peter; Ragab, Haissam; Yamamura, Jin; Halscheidt, Sandra; Labisch, Christoph; Sifa, Rafet: Segmentation and analysis of lumbar spine mri scans for vertebral body measurements, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2023, https://www.esann.org/sites/default/files/proceedings/2023/ES2023-88.pdf, Schneider.etal.2023b,
@Inproceedings{Schneider.etal.2023b,
author={Schneider, Helen; Biesner, David; Ashokan, Akash; Broß, Maximilian; Kador, Rebecca; Bagyo, Gabor; Dankerl, Peter; Ragab, Haissam; Yamamura, Jin; Halscheidt, Sandra; Labisch, Christoph; Sifa, Rafet},
title={Segmentation and analysis of lumbar spine mri scans for vertebral body measurements},
booktitle={European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
url={https://www.esann.org/sites/default/files/proceedings/2023/ES2023-88.pdf},
year={2023},
abstract={This paper investigates a data- and knowledge-driven approach to automatically analyze lumbar MRI scans. The dataset used is an in-house dataset of 142 sagital lumbar spine images from German radiology practices of the evidia GmbH. We implement state-of-the-art deep learning methods to segment the individual vertebral bodies. Overall, a very accurate segmentation performance of 97% Dice Score was...}}
