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Bone Fragment Segmentation Using Deep Interactive Object Selection
Linköping University, Department of Electrical Engineering, Computer Vision.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In recent years semantic segmentation models utilizing Convolutional Neural Networks (CNN) have seen significant success for multiple different segmentation problems. Models such as U-Net have produced promising results within the medical field for both regular 2D and volumetric imaging, rivalling some of the best classical segmentation methods.

In this thesis we examined the possibility of using a convolutional neural network-based model to perform segmentation of discrete bone fragments in CT-volumes with segmentation-hints provided by a user. We additionally examined different classical segmentation methods used in a post-processing refinement stage and their effect on the segmentation quality. We compared the performance of our model to similar approaches and provided insight into how the interactive aspect of the model affected the quality of the result.

We found that the combined approach of interactive segmentation and deep learning produced results on par with some of the best methods presented, provided there were adequate amount of annotated training data. We additionally found that the number of segmentation hints provided to the model by the user significantly affected the quality of the result, with convergence of the result around 8 provided hints.

Place, publisher, year, edition, pages
2019. , p. 78
Keywords [en]
Machine Learning, Convolutional Neural Networks, Computer Vision, Medical Image Segmentation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-157668ISRN: LiTH-ISY-EX--19/5197--SEOAI: oai:DiVA.org:liu-157668DiVA, id: diva2:1326942
External cooperation
Sectra AB
Subject / course
Computer science
Presentation
2019-05-07, Systemet, B-Huset, Linköpings universitet, Linköping, 15:15 (English)
Supervisors
Examiners
Available from: 2019-06-24 Created: 2019-06-18 Last updated: 2019-06-24Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
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
  • rtf