A hyperacute stroke segmentation method using 3D U-Net integrated with physicians' knowledge for NCCT

Takuya Fuchigami, Sadato Akahori, Takayuki Okatani, Yuanzhong Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)


Evaluating size of hyperacute stroke lesions speedily is an essential procedure before physicians make treatment decisions. For a patient with brain stroke suspicion, noncontrast computerized tomography (NCCT) is firstly taken for initial infarction assessment. However, in a lot of cases, because CT hypoattenuation and texture variation caused by hyperacute ischemia are subtle, besides local intensities and texture, physicians usually compare the difference between right and left sides based on the symmetric characteristic of brain anatomy not to miss the subtle lesions. In this paper, we propose a novel 3D U-Net architecture that integrates the comparison knowledge to automatically segment hyperacute stroke lesions on NCCT. To effectively capture right and left comparison features, we introduced a horizontal flip operation into 3D UNet. We also applied gradient-based sensitivity map method to our trained model in order to visualize how much each voxel contributes to segmentation results. Experimental results showed that the proposed architecture improved segmentation accuracy. Dice similarity coefficient (DSC) was improved from 0.44 to 0.54. Sensitivity and specificity was also improved from 0.80 to 1.00 and from 0.90 to 0.98 respectively. Sensitivity maps derived from our trained model demonstrated that both the right and left sides were utilized more effectively to successfully segment ischemic lesions.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
ISBN (Electronic)9781510633957
Publication statusPublished - 2020
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: 2020 Feb 162020 Feb 19

Publication series

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


ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
Country/TerritoryUnited States


  • automatic lesion segmentation
  • convolutional neural networks
  • deep learning interpretability
  • hyperacute stroke
  • noncontrast computerized tomography
  • symmetric characteristic


Dive into the research topics of 'A hyperacute stroke segmentation method using 3D U-Net integrated with physicians' knowledge for NCCT'. Together they form a unique fingerprint.

Cite this