Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients
This work addresses the challenge of accurate brain MRI registration for glioma patients, which is incremental as it enhances an existing method for a specific medical imaging task.
The paper tackled the problem of registering longitudinal brain MRI scans with pathologies like diffuse glioma, achieving a reduction in mean absolute error from 8.20 mm to 3.51 mm on the training set and 2.93 mm on the validation set.
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 the 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 is able to improve 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 de-formable 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.