DeepCERES: A Deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI

TL;DR

DeepCERES is a novel deep learning approach that leverages ultra-high resolution multimodal MRI data to accurately segment cerebellar lobules, outperforming existing methods and providing an accessible online tool.

Contribution

The paper introduces a new deep learning method using ultra-high resolution multimodal MRI and an ensemble of networks, with a novel architecture and integration of prior knowledge, for improved cerebellar lobule segmentation.

Findings

Enhanced segmentation accuracy over standard resolution methods

Memory-efficient ensemble of deep networks

Robustness improved by integrating multi-atlas prior knowledge

Abstract

This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution ($1 \text{ mm}^{3}$) or using mono-modal data, the proposed method improves cerebellum lobule segmentation through the use of a multimodal and ultra-high resolution ($0.125 \text{ mm}^{3}$) training dataset. To develop the method, first, a database of semi-automatically labelled cerebellum lobules was created to train the proposed method with ultra-high resolution T1 and T2 MR images. Then, an ensemble of deep networks has been designed and developed, allowing the proposed method to excel in the complex cerebellum lobule segmentation task, improving precision while being memory efficient. Notably, our approach deviates from the traditional U-Net model by exploring alternative architectures. We have also integrated deep learning with classical machine learning methods incorporating a priori knowledge from multi-atlas segmentation, which improved precision and robustness. Finally, a new online pipeline, named DeepCERES, has been developed to make available the proposed method to the scientific community requiring as input only a single T1 MR image at standard resolution.