Leif Østergaard

Better diffusion segmentation in acute ischemic stroke through automatic tree learning anomaly segmentation

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


  • Jens K. Boldsen
  • Thorbjørn S. Engedal
  • Salvador Pedraza, Department of Radiology, Girona Biomedical Research Institute, Hospital Universitari de Girona Dr Josep Trueta, Universitat de Girona, Girona
  • ,
  • Tae Hee Cho, Hospices Civils de Lyon, Université Lyon 1
  • ,
  • Götz Thomalla, University Medical Center Hamburg-Eppendorf
  • ,
  • Norbert Nighoghossian, Hospices Civils de Lyon, Université Lyon 1
  • ,
  • Jean Claude Baron, Université Descartes, Sorbonne Paris Cité, Cambridge University
  • ,
  • Jens Fiehler, University Medical Center Hamburg-Eppendorf
  • ,
  • Leif Østergaard
  • Kim Mouridsen

Stroke is the second most common cause of death worldwide, responsible for 6.24 million deaths in 2015 (about 11% of all deaths). Three out of four stroke survivors suffer long term disability, as many cannot return to their prior employment or live independently. Eighty-seven percent of strokes are ischemic. As an increasing volume of ischemic brain tissue proceeds to permanent infarction in the hours following the onset, immediate treatment is pivotal to increase the likelihood of good clinical outcome for the patient. Triaging stroke patients for active therapy requires assessment of the volume of salvageable and irreversible damaged tissue, respectively. With Magnetic Resonance Imaging (MRI), diffusion-weighted imaging is commonly used to assess the extent of permanently damaged tissue, the core lesion. To speed up and standardize decision-making in acute stroke management we present a fully automated algorithm, ATLAS, for delineating the core lesion. We compare performance to widely used threshold based methodology, as well as a recently proposed state-of-the-art algorithm: COMBAT Stroke. ATLAS is a machine learning algorithm trained to match the lesion delineation by human experts. The algorithm utilizes decision trees along with spatial pre-and post-regularization to outline the lesion. As input data the algorithm takes images from 108 patients with acute anterior circulation stroke from the I-Know multicenter study. We divided the data into training and test data using leave-one-out cross validation to assess performance in independent patients. Performance was quantified by the Dice index. The median Dice coefficient of ATLAS algorithm was 0.6122, which was significantly higher than COMBAT Stroke, with a median Dice coefficient of 0.5636 (p < 0.0001) and the best possible performing methods based on thresholding of the diffusion weighted images (median Dice coefficient: 0.3951) or the apparent diffusion coefficient (median Dice coefficeint: 0.2839). Furthermore, the volume of the ATLAS segmentation was compared to the volume of the expert segmentation, yielding a standard deviation of the residuals of 10.25 ml compared to 17.53 ml for COMBAT Stroke. Since accurate quantification of the volume of permanently damaged tissue is essential in acute stroke patients, ATLAS may contribute to more optimal patient triaging for active or supportive therapy.

Original languageEnglish
Article number21
JournalFrontiers in Neuroinformatics
Publication statusPublished - 25 Apr 2018

    Research areas

  • Computer learning, Decision trees, Diffusion lesion, Diffusion MRI, Segmentation, Stroke

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