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Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients

Zeineldin RA, Karar ME, Mathis-Ullrich F, Burgert O (2023)

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Publication Type: Conference contribution

Publication year: 2023

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 14092 LNCS

Pages Range: 25-34

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Singapore, SGP

ISBN: 9783031441523

DOI: 10.1007/978-3-031-44153-0_3

Abstract

Reliable and accurate registration of patient-specific brain magnetic resonance imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes. This paper describes our contribution to the registration of the longitudinal brain MRI task of the Brain Tumor Sequence Registration Challenge 2022 (BraTS-Reg 2022). We developed an enhanced unsupervised learning-based method that extends our previously developed registration framework iRegNet. In particular, incorporating an unsupervised learning-based paradigm as well as several minor modifications to the network pipeline, allows the enhanced iRegNet method to achieve respectable results. Experimental findings show that the enhanced self-supervised model improves the initial mean median registration absolute error (MAE) from 8.20 ± 7.62 mm to the lowest value of 3.51 ± 3.50 for the training set while achieving an MAE of 2.93 ± 1.63 mm for the validation set. Additional qualitative validation of this study was conducted through overlaying pre-post MRI pairs before and after the deformable registration. The proposed method scored 5th place during the testing phase of the MICCAI BraTS-Reg 2022 challenge. The docker image to reproduce our BraTS-Reg submission results will be publicly available.

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How to cite

APA:

Zeineldin, R.A., Karar, M.E., Mathis-Ullrich, F., & Burgert, O. (2023). Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients. In Spyridon Bakas, Ujjwal Baid, Bhakti Baheti, Alessandro Crimi, Sylwia Malec, Monika Pytlarz, Maximilian Zenk, Reuben Dorent (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 25-34). Singapore, SGP: Springer Science and Business Media Deutschland GmbH.

MLA:

Zeineldin, Ramy A., et al. "Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients." Proceedings of the 8th International MICCAI Brainlesion Workshop, BrainLes 2022, Singapore, SGP Ed. Spyridon Bakas, Ujjwal Baid, Bhakti Baheti, Alessandro Crimi, Sylwia Malec, Monika Pytlarz, Maximilian Zenk, Reuben Dorent, Springer Science and Business Media Deutschland GmbH, 2023. 25-34.

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