Evaluation of Diffusion Lesion Volume Measurements in Acute Ischemic Stroke Using Encoder-Decoder Convolutional Network

Yoon Chul Kim, Ji Eun Lee, Inwu Yu, Ha Na Song, In Young Baek, Joon Kyung Seong, Han Gil Jeong, Beom Joon Kim, Hyo Suk Nam, Jong Won Chung, Oh Young Bang, Gyeong Moon Kim, Woo Keun Seo

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


Background and Purpose-Automatic segmentation of cerebral infarction on diffusion-weighted imaging (DWI) is typically performed based on a fixed apparent diffusion coefficient (ADC) threshold. Fixed ADC threshold methods may not be accurate because ADC values vary over time after stroke onset. Deep learning has the potential to improve the accuracy, provided that a large set of correctly annotated lesion data is used for training. The purpose of this study was to evaluate deep learning-based methods and compare them with commercial software in terms of lesion volume measurements. Methods-U-net, an encoder-decoder convolutional neural network, was adopted to train segmentation models. Two U-net models were developed: a U-net (DWI+ADC) model, trained on DWI and ADC data, and a U-net (DWI) model, trained on DWI data only. A total of 296 subjects were used for training and 134 for external validation. An expert neurologist manually delineated the stroke lesions on DWI images, which were used as the ground-truth reference. Lesion volume measurements from the U-net methods were compared against the expert's manual segmentation and Rapid Processing of Perfusion and Diffusion (RAPID; iSchemaView Inc) analysis. Results-In external validation, U-net (DWI+ADC) showed the highest intraclass correlation coefficient with manual segmentation (intraclass correlation coefficient, 1.0; 95% CI, 0.99-1.00) and sufficiently high correlation with the RAPID results (intraclass correlation coefficient, 0.99; 95% CI, 0.98-0.99). U-net (DWI+ADC) and manual segmentation resulted in the smallest 95% Bland-Altman limits of agreement (-5.31 to 4.93 mL) with a mean difference of-0.19 mL. Conclusions-The presented deep learning-based method is fully automatic and shows a high correlation of diffusion lesion volume measurements with manual segmentation and commercial software. The method has the potential to be used in patient selection for endovascular reperfusion therapy in the late time window of acute stroke.

Original languageEnglish
Pages (from-to)1444-1451
Number of pages8
Issue number6
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) and funded by the Korea government (grant numbers NRF-2017R1A2B4010648 and NRF-2018R1D1A1B07042692).

Funding Information:
Dr Seo received honoraria for lectures from Pfizer, Sanofi-Aventis, Otsuka Korea, Dong-A Pharmaceutical Co, Ltd, Beyer, Daewoong pharmaceutical Co, Ltd, Daiichi Sankyo Korea Co, Ltd, Boryung pharmaceutical, study grant from Daiichi Sankyo Korea Co, Ltd, and consulting fee from OBELAB Inc. The other authors report no conflicts.

Publisher Copyright:
© 2019 American Heart Association, Inc.


  • cerebral infarction
  • deep learning
  • diffusion
  • ischemia
  • neurologist

ASJC Scopus subject areas

  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine
  • Advanced and Specialised Nursing


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