Original title: Segmentation Of Cartilage Tissue In Micro Ct Images Of Mouse Embryos With Modified U-Net Convolutional Neural Network
Authors: Matula, Jan
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Manual segmentation of cartilage tissue in micro CT images of mouse embryos is a very time-consuming process and significantly increases the time required for the research of mammal facial structure development. It is possible to solve this problem by using a fully-automatic segmentation algorithm. In this paper, a fully-automatic segmentation method is proposed using a convolutional neural network trained on manually segmented data. The architecture of the proposed convolutional network is based on the U-Net architecture with its encoding part substituted for the encoding part of the VGG16 classification convolutional neural network pre-trained on the ImageNet database of labelled images. The proposed network achieves average Dice coefficient 0.88 in comparison to manually segmented images.
Keywords: cartilage; convolutional neural networks; deep learning; segmentation
Host item entry: Proceedings of the 25st Conference STUDENT EEICT 2019, ISBN 978-80-214-5735-5

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/186650

Permalink: http://www.nusl.cz/ntk/nusl-414552


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