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Automated Kidney Segmentation in Magnetic Resonance Imaging using U-Net
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Manual analysis of medical images such as magnetic resonance imaging (MRI) requires a trained professional, is time-consuming and results may vary between experts. We propose an automated method for kidney segmentation using a convolutional Neural Network (CNN) model based on the U-Net architecture. Investigations are done to compare segmentations between trained experts, inexperienced operators and the Neural Network model, showing near human expert level performance from the Neural Network. Stratified sampling is performed when selecting which subject volumes to perform manual segmentations on to create training data. Experiments are run to test the effectiveness of transfer learning and data augmentation and we show that one of the most important components of a successful machine learning pipeline is larger quantities of carefully annotated data for training.

Place, publisher, year, edition, pages
2019. , p. 22
Keywords [en]
Deep learning, U-Net, Computer Vision, Organ segmentation, Magnetic Resonance Imaging
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-391269OAI: oai:DiVA.org:uu-391269DiVA, id: diva2:1344549
External cooperation
Uppsala Clinical Research; Department of Surgical Sciences, Section of Radiology; Antaros Medical
Subject / course
Statistics
Educational program
Master Programme in Statistics
Supervisors
Examiners
Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-08-21Bibliographically approved

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fulltext(2282 kB)28 downloads
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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