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2018 | OriginalPaper | Chapter

Automatic Segmentation of Corneal Endothelium Images with Convolutional Neural Network

Author : Karolina Nurzynska

Published in: Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety

Publisher: Springer International Publishing

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Abstract

A fully-automatic segmentation of corneal endothelial images is addressed in this paper. It can find its application in the medicine removing the burden of manual annotations from the physicians allowing for faster patient diagnosis. The proposed system is based on pre-trained convolutional neural network AlexNet and uses a transfer learning methodology to build a system for delineation of endothelial cells. The training is based on the classification of small patches of an image which represents cell body or cell border class. The validation set proved that 99% correct classification ratio accuracy and F1 score were achieved. Exploiting this network in a system configured for segmentation it proved very good detection of cell bodies and supported by best-fit skeletonization allowed to locate cell borders precisely.

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Title: Automatic Segmentation of Corneal Endothelium Images with Convolutional Neural Network
Author: Karolina Nurzynska
Publisher: Springer International Publishing
Book: Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety
Print ISBN: 978-3-319-99986-9

Electronic ISBN: 978-3-319-99987-6

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-99987-6_25

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