Evaluation of U-net segmentation models for infarct volume measurement in acute ischemic stroke: Comparison with fixed ADC threshold-based methods

Yoon Chul Kim, Ji Eun Lee, Inwu Yu, In Young Baek, Han Gil Jeong, Beom Joon Kim, Joon Kyung Seong, Jong Won Chung, Oh Young Bang, Woo Keun Seo

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Ischemic stroke volume is a strong predictor of functional outcome and may play a role in decision making of reperfusion therapy in the late time window (< 6hr of stroke onset to MRI time) when it is obtained along with penumbra volume. Automatic diffusion lesion segmentation can be performed using a commercial software package and is typically based on a fixed apparent diffusion coefficient (ADC) threshold. ADC values alone may not be guaranteed to be highly accurate in the identification of diffusion lesions. Deep learning has the potential to improve the accuracy of diffusion lesion segmentation, provided that a large set of correctly labeled lesion mask data is used for training. The purpose of this study is to evaluate deep learning-based segmentation methods and compare them with three fixed ADC threshold-based methods. U-net was adopted to train a segmentation model. Two U-net models were developed: a model "U-net (DWI+ADC)" trained from DWI and ADC data, and a model "U-net (DWI)" trained from DWI data only. 296 subjects were used for training, and 134 subjects were used for testing. An expert neurologist manually delineated infarct masks on DWI, which served as ground-truth reference. Lesion volume measurements from the two U-net methods and three fixed ADC threshold-based methods were compared against lesion volume measurements from manual segmentation. In testing, the "U-net (DWI+ADC)" method outperformed other methods in lesion volume measurement, with the smallest root-mean-square error of 2.96 ml and the highest Pearson correlation coefficient of 0.997. The proposed method has the potential to automatically measure diffusion lesion volume in a fast and accurate manner, in patients with acute ischemic stroke.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Horst K. Hahn
ISBN (Electronic)9781510625471
Publication statusPublished - 2019
EventMedical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States
Duration: 2019 Feb 172019 Feb 20

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2019: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

Funding Information:
This study was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2017R1A2B4010648, 2018R1D1A1B07042692).

Publisher Copyright:
© 2019 SPIE.


  • Brain
  • Deep learning
  • Diffusion weighted imaging
  • Image segmentation
  • Ischemia
  • Stroke

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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