Utilizing Average Symmetrical Surface Distance in Active Shape Modeling for Subcortical Surface Generation with Slow-fast Learning

Zhong, Pinyuan1; Cheng, Ran2; Tang, Xiaoying1

10.1109/EMBC48229.2022.9871829

2022

44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (IEEE EMBC)

2375-7477

978-1-7281-2783-5

44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (IEEE EMBC)

230-233

11-15 July 2022

Glasgow, Scotland, United Kingdom

In this paper, we propose and validate an automatic pipeline for subcortical surface generation by making use of the average symmetrical surface distance (ASSD) loss in active shape modeling (ASM). A group of template surfaces are first generated via large deformation diffeomorphic metric mapping based surface deformation. ASM is then employed to obtain the mean shape and shape variation parameters of the template surfaces. To obtain the optimal shape variation parameters which best fit the target structure after acting upon the mean shape, a recently proposed derivative-free optimization method (the slow-fast learning method) is adopted. The ASSD loss, in addition to the typically utilized Dice similarity coefficient loss, is employed during the learning process to help enhance the boundary accuracy. We successfully validate the importance of the ASSD loss through ablation analysis. In addition, we show the effectiveness of the slow-fast learning method by comparing it with other state-of-the-art derivative-free optimization algorithms. Our proposed pipeline is found to be capable of yielding subcortical surfaces with high accuracy, correct anatomical topology, and sufficient smoothness. Clinical Relevance- This work provides a useful tool for generating subcortical surfaces which are important biomarkers for a variety of brain disorders.

Learning Systems Measurement Shape Pipelines Optimization Methods Surface Fitting Topology

第一 ; 通讯

CPCI-S ; EI

IEEE

1.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
2.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Zhong, Pinyuan,Cheng, Ran,Tang, Xiaoying. Utilizing Average Symmetrical Surface Distance in Active Shape Modeling for Subcortical Surface Generation with Slow-fast Learning[C],2022:230-233.