Automatic tissue image segmentation based on image processing and deep learning

Zhenglun Kong; Junyi Luo; Shengpu Xu; Ting Li

doi:10.1117/12.2293481

12 February 2018 Automatic tissue image segmentation based on image processing and deep learning

Zhenglun Kong, Junyi Luo, Shengpu Xu, Ting Li

Proceedings Volume 10481, Neural Imaging and Sensing 2018; 104811T (2018) https://doi.org/10.1117/12.2293481
Event: SPIE BiOS, 2018, San Francisco, California, United States

Abstract

Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies or other novel imaging technologies. Plus, image segmentation also provides detailed structure description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation method. Here we used image enhancement, operators, and morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in a deep learning way. We also introduced parallel computing. Such approaches greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. Our results can be used as a criteria when diagnosing diseases such as cerebral atrophy, which is caused by pathological changes in gray matter or white matter. We demonstrated the great potential of such image processing and deep leaning combined automatic tissue image segmentation in personalized medicine, especially in monitoring, and treatments.

Citation Download Citation

Zhenglun Kong, Junyi Luo, Shengpu Xu, and Ting Li "Automatic tissue image segmentation based on image processing and deep learning", Proc. SPIE 10481, Neural Imaging and Sensing 2018, 104811T (12 February 2018); https://doi.org/10.1117/12.2293481

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