Rodrigues Livia, Rezende Thiago Junqueira Ribeiro, Wertheimer Guilherme, Santos Yves, França Marcondes, Rittner Leticia
Medical Image Computing Lab, School of Electrical and Computer Engineering (FEEC), University of Campinas, Albert Einstein Street, 400, Campinas, SP 13083-887, Brazil.
Department of Neurology, School of Medical Sciences, University of Campinas, Tessalia Vieira de Camargo Street, 126, Campinas, SP 13083-887, Brazil.
Neuroimage. 2022 Dec 1;264:119741. doi: 10.1016/j.neuroimage.2022.119741. Epub 2022 Nov 8.
The hypothalamus is a small brain structure that plays essential roles in sleep regulation, body temperature control, and metabolic homeostasis. Hypothalamic structural abnormalities have been reported in neuropsychiatric disorders, such as schizophrenia, amyotrophic lateral sclerosis, and Alzheimer's disease. Although mag- netic resonance (MR) imaging is the standard examination method for evaluating this region, hypothalamic morphological landmarks are unclear, leading to subjec- tivity and high variability during manual segmentation. Due to these limitations, it is common to find contradicting results in the literature regarding hypothalamic volumetry. To the best of our knowledge, only two automated methods are available in the literature for hypothalamus segmentation, the first of which is our previous method based on U-Net. However, both methods present performance losses when predicting images from different datasets than those used in training. Therefore, this project presents a benchmark consisting of a diverse T1-weighted MR image dataset comprising 1381 subjects from IXI, CC359, OASIS, and MiLI (the latter created specifically for this benchmark). All data were provided using automatically generated hypothalamic masks and a subset containing manually annotated masks. As a baseline, a method for fully automated segmentation of the hypothalamus on T1-weighted MR images with a greater generalization ability is presented. The pro- posed method is a teacher-student-based model with two blocks: segmentation and correction, where the second corrects the imperfections of the first block. After using three datasets for training (MiLI, IXI, and CC359), the prediction performance of the model was measured on two test sets: the first was composed of data from IXI, CC359, and MiLI, achieving a Dice coefficient of 0.83; the second was from OASIS, a dataset not used for training, achieving a Dice coefficient of 0.74. The dataset, the baseline model, and all necessary codes to reproduce the experiments are available at https://github.com/MICLab-Unicamp/HypAST and https://sites.google.com/ view/calgary-campinas-dataset/hypothalamus-benchmarking. In addition, a leaderboard will be maintained with predictions for the test set submitted by anyone working on the same task.
下丘脑是一个位于脑内的小结构，在睡眠调节、体温控制和代谢稳态中发挥着重要作用。下丘脑结构异常已在神经精神疾病中被报道，如精神分裂症、肌萎缩侧索硬化症和阿尔茨海默病。尽管磁共振（MR）成像为评估该区域的标准检查方法，但下丘脑形态学标志不清晰，导致手动分割时主观性强且变异性高。由于这些局限性，文献中关于下丘脑体积测量的结果往往相互矛盾。据我们所知，文献中仅有两种用于下丘脑分割的自动化方法，第一种是我们之前基于U-Net的方法。然而，当预测来自与训练集不同的数据集的图像时，这两种方法的性能都会下降。因此，本项目提供了一个基准，该基准包含一个多样的T1加权MR图像数据集，包括来自IXI、CC359、OASIS和MiLI（后者是专门为此基准创建的）的1381名受试者。所有数据都提供了自动生成的下丘脑掩码以及一个包含手动标注掩码的子集。作为基线，提出了一种在T1加权MR图像上对下丘脑进行全自动分割且具有更强泛化能力的方法。所提出的方法是一种基于师生模型，有两个模块：分割和校正，其中第二个模块用于校正第一个模块的缺陷。在使用三个数据集（MiLI、IXI和CC359）进行训练后，在两个测试集上测量了模型的预测性能：第一个测试集由来自IXI、CC359和MiLI的数据组成，Dice系数达到0.83；第二个测试集来自未用于训练的OASIS数据集，Dice系数为0.74。数据集、基线模型以及重现实验所需的所有代码可在https://github.com/MICLab-Unicamp/HypAST和https://sites.google.com/ view/calgary-campinas-dataset/hypothalamus-benchmarking获取。此外，将维护一个排行榜，记录任何从事相同任务的人对测试集的预测结果。
