Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus

Frontiers in Neuroscience

卷:14

Chunliang Wang¹  Örjan Smedby¹  Olof Lindberg²  J-Sebastian Muehlboeck²  Eric Westman²  Irene Brusini² 

[1] Division of Biomedical Imaging, Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden;

[2] Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Solna, Sweden;

关键词: hippocampus;    brain MRI;    Alzheimer’s disease;    image segmentation;    deep learning;    statistical shape model;   

DOI  :  10.3389/fnins.2020.00015

来源: DOAJ

【 摘 要 】

Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer’s disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.

【 授权许可】

Unknown