Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing

TL;DR

This paper introduces Neural Pre-processing (NPP), an end-to-end weakly supervised neural network framework that simultaneously performs skull-stripping, intensity normalization, and spatial normalization of brain MRI images, outperforming existing methods.

Contribution

The paper presents a novel end-to-end learning framework for brain MRI pre-processing that handles multiple sub-tasks jointly without individual supervision, with flexible inference control.

Findings

NPP outperforms state-of-the-art single-task methods.

Explicit disentanglement improves model performance.

Architecture design is crucial for effectiveness.

Abstract

Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space. In this paper, we propose an end-to-end weakly supervised learning approach, called Neural Pre-processing (NPP), for solving all three sub-tasks simultaneously via a neural network, trained on a large dataset without individual sub-task supervision. Because the overall objective is highly under-constrained, we explicitly disentangle geometric-preserving intensity mapping (skull-stripping and intensity normalization) and spatial transformation (spatial normalization). Quantitative results show that our model outperforms state-of-the-art methods which tackle only a single sub-task. Our ablation experiments demonstrate the importance of the architecture design we chose for NPP. Furthermore, NPP affords the user the flexibility to control each of these tasks at inference time. The code and model are freely-available at \url{https://github.com/Novestars/Neural-Pre-processing}.