A benchmark for hypothalamus segmentation on T1-weighted MR images

水准点(测量) 分割 人工智能 计算机科学 模式识别(心理学) 心理学 地图学 地理
作者
Lívia Rodrigues,Thiago Junqueira Ribeiro de Rezende,Guilherme Soares de Oliveira Wertheimer,Yves Santos,Marcondes C. França,Letícia Rittner
出处
期刊:NeuroImage [Elsevier BV]
卷期号:264: 119741-119741 被引量:7
标识
DOI:10.1016/j.neuroimage.2022.119741
摘要

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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
blueskyzhi发布了新的文献求助10
1秒前
nihaoxiaoai完成签到,获得积分10
1秒前
1秒前
2秒前
烟花应助科研通管家采纳,获得10
4秒前
生动路人应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
4秒前
4秒前
CodeCraft应助科研通管家采纳,获得10
4秒前
量子星尘发布了新的文献求助10
4秒前
huhu完成签到,获得积分20
4秒前
Bio应助guozizi采纳,获得30
5秒前
Lemon发布了新的文献求助10
5秒前
高成浩完成签到,获得积分10
7秒前
小蘑菇应助慕子哥采纳,获得10
8秒前
9秒前
10秒前
兴奋念真发布了新的文献求助10
10秒前
Liangyong_Fu发布了新的文献求助30
11秒前
青柠完成签到,获得积分10
12秒前
monthli完成签到,获得积分10
12秒前
kinase完成签到 ,获得积分10
12秒前
13秒前
高成浩发布了新的文献求助10
14秒前
黑山老妖发布了新的文献求助10
14秒前
illusion完成签到,获得积分10
15秒前
15秒前
星辰大海应助qqqq采纳,获得10
17秒前
17秒前
17秒前
zzz发布了新的文献求助20
17秒前
18秒前
19秒前
海纳百川完成签到,获得积分10
19秒前
LONG发布了新的文献求助10
21秒前
板凳发布了新的文献求助30
21秒前
BCEMTZ完成签到,获得积分10
22秒前
黑山老妖完成签到,获得积分10
22秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Social Research Methods (4th Edition) by Maggie Walter (2019) 2390
A new approach to the extrapolation of accelerated life test data 1000
北师大毕业论文 基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 390
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Robot-supported joining of reinforcement textiles with one-sided sewing heads 360
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4010191
求助须知:如何正确求助?哪些是违规求助? 3550174
关于积分的说明 11305110
捐赠科研通 3284653
什么是DOI,文献DOI怎么找? 1810748
邀请新用户注册赠送积分活动 886556
科研通“疑难数据库(出版商)”最低求助积分说明 811451