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Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-Specific Interactive Refinement

  • Ashis Kumar DharaEmail author
  • Kalyan Ram Ayyalasomayajula
  • Erik Arvids
  • Markus Fahlström
  • Johan Wikström
  • Elna-Marie Larsson
  • Robin Strand
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ashis Kumar Dhara
    • 1
    Email author
  • Kalyan Ram Ayyalasomayajula
    • 1
  • Erik Arvids
    • 2
  • Markus Fahlström
    • 2
  • Johan Wikström
    • 2
  • Elna-Marie Larsson
    • 2
  • Robin Strand
    • 1
    • 2
  1. 1.Centre for Image AnalysisUppsala UniversityUppsalaSweden
  2. 2.Department of Surgical Sciences, RadiologyUppsala UniversityUppsalaSweden

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