DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI

TL;DR

DeepThalamus introduces a deep learning approach for automatic segmentation of brain thalamic nuclei using multimodal ultra-high resolution MRI, improving accuracy and robustness over existing methods and providing a pipeline for standard resolution data.

Contribution

The paper presents a novel multimodal deep neural network for ultra-high resolution thalamic segmentation, including a semi-supervised training strategy and a pipeline for standard resolution images.

Findings

Competitive segmentation accuracy compared to state-of-the-art methods

Robustness improved through semi-supervised training

Pipeline enables use with standard resolution MRI data

Abstract

The implication of the thalamus in multiple neurological pathologies makes it a structure of interest for volumetric analysis. In the present work, we have designed and implemented a multimodal volumetric deep neural network for the segmentation of thalamic nuclei at ultra-high resolution (0.125 mm3). Current tools either operate at standard resolution (1 mm3) or use monomodal data. To achieve the proposed objective, first, a database of semiautomatically segmented thalamic nuclei was created using ultra-high resolution T1, T2 and White Matter nulled (WMn) images. Then, a novel Deep learning based strategy was designed to obtain the automatic segmentations and trained to improve its robustness and accuaracy using a semisupervised approach. The proposed method was compared with a related state-of-the-art method showing competitive results both in terms of segmentation quality and efficiency. To make the proposed method fully available to the scientific community, a full pipeline able to work with monomodal standard resolution T1 images is also proposed.