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.

翻译：丘脑在多种神经系统疾病中的参与使其成为体积分析关注的重要结构。本研究设计并实现了一种多模态体积深度神经网络，用于超高分辨率（0.125 mm³）下丘脑核团的分割。现有工具要么仅在标准分辨率（1 mm³）下运行，要么仅使用单模态数据。为实现该目标，首先利用超高分辨率T1、T2及白质抑制（WMn）图像创建了半自动分割的丘脑核团数据库。随后设计了一种基于深度学习的新型自动分割策略，并通过半监督方法进行训练以提升其鲁棒性和准确性。将所提方法与现有最优方法进行对比，结果显示其在分割质量与效率方面均具有竞争力。为促进该方法的科学社区共享，本研究还提出了一套完整的处理流程，可兼容单模态标准分辨率T1图像。