Large-scale parcellation of the ventricular system using convolutional neural networks

Hans E. Atlason; Muhan Shao; Vidar Robertsson M.D.; Sigurdur Sigurdsson; Vilmundur Gudnason M.D.; Jerry L. Prince; Lotta M. Ellingsen

doi:10.1117/12.2514590

15 March 2019 Large-scale parcellation of the ventricular system using convolutional neural networks

Hans E. Atlason, Muhan Shao, Vidar Robertsson M.D., Sigurdur Sigurdsson, Vilmundur Gudnason M.D., Jerry L. Prince, Lotta M. Ellingsen

Author Affiliations +

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109530N (2019) https://doi.org/10.1117/12.2514590
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Enlarged ventricles are a marker of several brain diseases; however, they are also associated with normal aging. Better understanding of the distribution of ventricular sizes in a large population would be of great clinical value to robustly define imaging markers that distinguish health and disease. The AGES-Reykjavik study includes magnetic resonance imaging scans of 4811 individuals from an elderly Icelandic population. Automated brain segmentation algorithms are necessary to analyze such a large data set but state-of-the-art algorithms often require long processing times or depend on large manually annotated data sets when based on deep learning approaches. In an effort to increase robustness, decrease processing time, and avoid tedious manual delineations, we selected 60 subjects with a large range of ventricle sizes and generated training labels using an automated whole brain segmentation algorithm designed for brains with ventriculomegaly. Lesion labels were added to the training labels, which were subsequently used to train a patch-based three-dimensional U-net Convolutional Neural Network for very fast and robust labeling of the remaining subjects. Comparisons with ground truth manual labels demonstrate that the proposed method yields robust segmentation and labeling of the four main sub-compartments of the ventricular system.

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Hans E. Atlason, Muhan Shao, Vidar Robertsson M.D., Sigurdur Sigurdsson, Vilmundur Gudnason M.D., Jerry L. Prince, and Lotta M. Ellingsen "Large-scale parcellation of the ventricular system using convolutional neural networks", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109530N (15 March 2019); https://doi.org/10.1117/12.2514590

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