Original title: Analysis Of The Training Quality Of Brain Tumour Segmentation In Deep Learning Through Similarity
Authors: Ustsinau, Usevalad
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Manual segmentation of brain tumours in MR images is a time-consuming process, which increases the required time for the research of tumour development and its lesion on the cognitive functions of human. Recently there were developed modern solutions for this problem by using a fully automatic segmentation algorithm. As far as segmentation quality plays a highly important role for doctors, we have to train such a model with a significant amount of care to quality. In this paper, it is provided with an analysis of the training quality using state-of-art technology - convolutional neural network U-Net and with training on manually segmented data. The experiment has shown the effectiveness of the provided model and performed 50 training cases with the following analysis through the similarity. The results were put on the similarity matrix and dendrogram. The proposed outcome gives us certain ideas for future improving the quality of image segmentation.
Keywords: convolutional neural networks; deep learning; segmentation; U-Net
Host item entry: Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers, ISBN 978-80-214-5867-3

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/11012/200565

Permalink: http://www.nusl.cz/ntk/nusl-447617


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