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Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-specific Interactive Refinement
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.ORCID iD: 0000-0002-2502-6026
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2019 (English)In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries / [ed] Crimi A., Bakas S., Kuijf H., Keyvan F., Reyes M & van Walsum T., Cham: Springer, 2019, p. 115-122Conference paper, Published paper (Refereed)
Abstract [en]

Accurate volumetric change estimation of glioblastoma is very important for post-surgical treatment follow-up. In this paper, an interactive segmentation method was developed and evaluated with the aim to guide volumetric estimation of glioblastoma. U-Net based fully convolutional network is used for initial segmentation of glioblastoma from post contrast MR images. The max flow algorithm is applied on the probability map of U-Net to update the initial segmentation and the result is displayed to the user for interactive refinement. Network update is performed based on the corrected contour by considering patient specific learning to deal with large context variations among different images. The proposed method is evaluated on a clinical MR image database of 15 glioblastoma patients with longitudinal scan data. The experimental results depict an improvement of segmentation performance due to patient specific fine-tuning. The proposed method is computationally fast and efficient as compared to state-of-the-art interactive segmentation tools. This tool could be useful for post-surgical treatment follow-up with minimal user intervention.

Place, publisher, year, edition, pages
Cham: Springer, 2019. p. 115-122
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 11383
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-366550DOI: 10.1007/978-3-030-11723-8_11ISI: 000612997600011ISBN: 978-3-030-11722-1 (print)ISBN: 978-3-030-11723-8 (electronic)OAI: oai:DiVA.org:uu-366550DiVA, id: diva2:1264853
Conference
4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018
Funder
Swedish Research Council, 2014-6199Vinnova, 2017-02447Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2021-03-25Bibliographically approved

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Dhara, Ashis KumarAyyalasomayajula, Kalyan RamFahlström, MarkusWikström, JohanLarsson, Elna-MarieStrand, Robin

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Dhara, Ashis KumarAyyalasomayajula, Kalyan RamFahlström, MarkusWikström, JohanLarsson, Elna-MarieStrand, Robin
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Computerized Image Analysis and Human-Computer InteractionDivision of Visual Information and InteractionRadiology
Medical Image Processing

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