Keywords: brain MRI, preprocessing, nn-Unet
TL;DR: We show that skipping all steps excluding image alignment and voxel resampling from brain MRI deep learning pipeline may reduce computational costs and improve reproducibility across studies.
Abstract: Magnetic resonance imaging (MRI) data is heterogeneous due to the differences in device manufacturers, scanning protocols, and inter-subject variability. Although preprocessing pipeline standardizes image appearance, its influence on the quality of image segmentation on deep neural networks (DNN) has never been rigorously studied. Here we report a comprehensive study of multimodal MRI brain cancer image segmentation on TCIA-GBM open-source dataset. Our results that the most popular standardization steps add no value to artificial neural network performance; moreover, preprocessing can hamper model performance. We show that the only essential transformation for accurate analysis is the unification of voxel spacing across the dataset.
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Paper Type: recently published or submitted journal contributions
Primary Subject Area: Segmentation
Secondary Subject Area: Image Registration
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Code And Data: https://github.com/MedImAIR/brain-mri-processing-pipeline