TY - GEN
T1 - Surface Foliation Based Brain Morphometry Analysis
AU - Wen, Chengfeng
AU - Lei, Na
AU - Ma, Ming
AU - Qi, Xin
AU - Zhang, Wen
AU - Wang, Yalin
AU - Gu, Xianfeng
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Brain morphometry plays a fundamental role in neuroimaging research. In this work, we propose a novel method for brain surface morphometry analysis based on surface foliation theory. Given brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for morphometry analysis purpose. In this work, we propose a set of novel surface features. To the best of our knowledge, this is the first work to make use of surface foliation theory for brain morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying brain cortical surfaces between patients with Alzheimer’s disease and healthy control subjects demonstrate the efficiency and efficacy of our method.
AB - Brain morphometry plays a fundamental role in neuroimaging research. In this work, we propose a novel method for brain surface morphometry analysis based on surface foliation theory. Given brain cortical surfaces with automatically extracted landmark curves, we first construct finite foliations on surfaces. A set of admissible curves and a height parameter for each loop are provided by users. The admissible curves cut the surface into a set of pairs of pants. A pants decomposition graph is then constructed. Strebel differential is obtained by computing a unique harmonic map from surface to pants decomposition graph. The critical trajectories of Strebel differential decompose the surface into topological cylinders. After conformally mapping those topological cylinders to standard cylinders, parameters of standard cylinders (height, circumference) are intrinsic geometric features of the original cortical surfaces and thus can be used for morphometry analysis purpose. In this work, we propose a set of novel surface features. To the best of our knowledge, this is the first work to make use of surface foliation theory for brain morphometry analysis. The features we computed are intrinsic and informative. The proposed method is rigorous, geometric, and automatic. Experimental results on classifying brain cortical surfaces between patients with Alzheimer’s disease and healthy control subjects demonstrate the efficiency and efficacy of our method.
KW - Alzheimer disease
KW - Brain morphometry
KW - Shape classification
KW - Surface foliation
UR - http://www.scopus.com/inward/record.url?scp=85075560060&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075560060&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33226-6_20
DO - 10.1007/978-3-030-33226-6_20
M3 - Conference contribution
AN - SCOPUS:85075560060
SN - 9783030332259
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 186
EP - 195
BT - Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy - 4th International Workshop, MBIA 2019, and 7th International Workshop, MFCA 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Zhu, Dajiang
A2 - Yan, Jingwen
A2 - Huang, Heng
A2 - Shen, Li
A2 - Thompson, Paul M.
A2 - Westin, Carl-Fredrik
A2 - Pennec, Xavier
A2 - Joshi, Sarang
A2 - Nielsen, Mads
A2 - Sommer, Stefan
A2 - Fletcher, Tom
A2 - Durrleman, Stanley
PB - Springer
T2 - 4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -