A Deep Learning Based Framework for Joint Image Registration and Segmentation of Brain Metastases on Magnetic Resonance Imaging
Abstract
Manual segmentation of brain metastases (BM) is a laborious and time-consuming task for expert clinicians, especially in the setting of longitudinal patient imaging. Although automated deep learning (DL) approaches can segment larger lesions effectively, they suffer from poor sensitivity of lesion detection for micro-metastases. Moreover, these approaches segment all patient imaging independently of each other, ignoring relevant information from prior time-points. In order to utilize prior time-point information, we propose SPIRS, a joint image registration and segmentation method. Given a prior time-point image and segmentation mask (which are readily available in a routine clinical environment), we affinely and deformably register these to a new time-point image. This warped prior image and mask are then used to enhance and improve the segmentation of the new time-point. We apply SPIRS to a large retrospectively acquired single institution dataset and show that it outperforms current registration approaches on BM imaging and that it significantly improves segmentation performance for micro-metastatic lesions.
Cite
Text
Patel et al. "A Deep Learning Based Framework for Joint Image Registration and Segmentation of Brain Metastases on Magnetic Resonance Imaging." Proceedings of the 8th Machine Learning for Healthcare Conference, 2023.Markdown
[Patel et al. "A Deep Learning Based Framework for Joint Image Registration and Segmentation of Brain Metastases on Magnetic Resonance Imaging." Proceedings of the 8th Machine Learning for Healthcare Conference, 2023.](https://mlanthology.org/mlhc/2023/patel2023mlhc-deep/)BibTeX
@inproceedings{patel2023mlhc-deep,
title = {{A Deep Learning Based Framework for Joint Image Registration and Segmentation of Brain Metastases on Magnetic Resonance Imaging}},
author = {Patel, Jay and Ahmed, Syed Rakin and Chang, Ken and Singh, Praveer and Gidwani, Mishka and Hoebel, Katharina and Kim, Albert and Bridge, Christopher and Teng, Chung-Jen and Li, Xiaomei and Xu, Gongwen and McDonald, Megan and Aizer, Ayal and Bi, Wenya Linda and Ly, Ina and Rosen, Bruce and Brastianos, Priscilla and Huang, Raymond and Gerstner, Elizabeth and Kalpathy-Cramer, Jayashree},
booktitle = {Proceedings of the 8th Machine Learning for Healthcare Conference},
year = {2023},
pages = {565-587},
volume = {219},
url = {https://mlanthology.org/mlhc/2023/patel2023mlhc-deep/}
}