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Localization Network and End-to-End Cascaded U-Nets for Kidney Tumor Segmentation
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Eindhoven University of Technology, 5612 AZ Eindhoven, the Netherlands.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
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2019 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging. This is partly due to its challenging manual annotation and great medical impact. Within the scope of the Kidney Tumor Segmentation Challenge 2019, that is aiming at combined kidney and tumor segmentation, this work proposes a novel combination of 3D U-Nets---collectively denoted TuNet---utilizing the resulting kidney masks for the consecutive tumor segmentation. The proposed method achieves a Sørensen-Dice coefficient score of 0.902 for the kidney, and 0.408 for the tumor segmentation, computed from a five-fold cross-validation on the 210 patients available in the data.

Place, publisher, year, edition, pages
2019.
National Category
Computer Vision and Robotics (Autonomous Systems) Medical and Health Sciences
Identifiers
URN: urn:nbn:se:umu:diva-166757OAI: oai:DiVA.org:umu-166757DiVA, id: diva2:1381720
Conference
MICCAI 2019, Shenzhen, China, Oct 13-17, 2019
Available from: 2019-12-27 Created: 2019-12-27 Last updated: 2020-01-02Bibliographically approved

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Simkó, AttilaNyholm, TufveLöfstedt, Tommy

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