A deep learning based framework for joint image registration and segmentation of brain metastases on magnetic resonance imaging Conference Paper

  


Authors: Patel, J.; Ahmed, S. R.; Chang, K.; Singh, P.; Gidwani, M.; Hoebel, K.; Kim, A.; Bridge, C.; Teng, C. J.; Li, X.; Xu, G.; McDonald, M.; Aizer, A.; Bi, W. L.; Ly, I.; Rosen, B.; Brastianos, P.; Huang, R.; Gerstner, E.; Kalpathy-Cramer, J.
Title: A deep learning based framework for joint image registration and segmentation of brain metastases on magnetic resonance imaging
Conference Title: 8th Machine Learning for Healthcare Conference
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. © 2023 J. Patel1 et al.
Keywords: magnetic resonance imaging; pathology; image enhancement; brain; brain metastasis; time points; image registration; image segmentation; manual segmentation; time-consuming tasks; registration methods; deep learning; lesion detection; learning approach; large datasets; images segmentations; images registration; patient imaging
Journal Title Proceedings of Machine Learning Research
Volume: 219
Conference Dates: 2023 Aug 11-12
Conference Location: New York, NY
ISBN: 2640-3498
Publisher: Journal Machine Learning Research  
Date Published: 2023-01-01
Start Page: 565
End Page: 587
Language: English
PROVIDER: scopus
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184282035&partnerID=40&md5=c7d72b528a0ef668a5bd1423dc9f6402
Notes: Conference paper -- Source: Scopus