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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, Computerized Image Analysis and Human-Computer 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.
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2018 (English)In: Proceedings, Brain Lesion (BrainLes) workshop, 2018Conference 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 dierent images. The proposed method is evaluated on a clinical MR image databas eof 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-upwith minimal user intervention.

Place, publisher, year, edition, pages
2018.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-366550OAI: oai:DiVA.org:uu-366550DiVA, id: diva2:1264853
Conference
21st INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING & COMPUTER ASSISTED INTERVENTION, September 16-20, 2018, Granada, Spain
Funder
Swedish Research Council, 2014-6199Vinnova, 2017-02447
Note

Extended versions of all accepted papers will be published as LCNS proceedings by Springer-Verlag. http://www.brainlesion-workshop.org/

Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2019-03-14Bibliographically approved

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http://www.miccai2018.org/files/downloads/AppPDF/miccaikitap2018.pdf

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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 InteractionRadiology
Medical Image Processing

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