Metric Learning for Multi-atlas based Segmentation of Hippocampus

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 15; no. 1; pp. 41 - 50
• Main Authors: Zhu, Hancan, Cheng, Hewei, Yang, Xuesong, Fan, Yong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2017; Springer Nature B.V
• Subjects: Aged; Aged, 80 and over; Algorithms; Atlases as Topic; Bioinformatics; Biomedical and Life Sciences; Biomedicine; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Female; Hippocampus - cytology; Hippocampus - pathology; Humans; Image Processing, Computer-Assisted - methods; Machine Learning; Magnetic Resonance Imaging - methods; Male; Neurology; Neurosciences; Original Article; Software; Multi-atlas image segmentation; Hippocampus segmentation; Label fusion; Metric learning
• ISSN: 1539-2791; 1559-0089; 1559-0089
• DOI: 10.1007/s12021-016-9312-y

Automatic and reliable segmentation of hippocampus from MR brain images is of great importance in studies of neurological diseases, such as epilepsy and Alzheimer’s disease. In this paper, we proposed a novel metric learning method to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute a similarity measure between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated. The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. The proposed method has been validated for segmenting hippocampus based on the EADC-ADNI dataset with manually labelled hippocampus of 100 subjects. The experiment results demonstrated that our method achieved statistically significant improvement in segmentation accuracy, compared with state-of-the-art multi-atlas image segmentation methods.