Stenosis Detection from Time-of-Flight Magnetic Resonance Angiography via Deep Learning 3D Squeeze and Excitation Residual Networks

Hunjin Chung, Koung Mi Kang, Mohammed A. Al-Masni, Chul Ho Sohn, Yoonho Nam, Kanghyun Ryu, Dong Hyun Kim

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Intracranial artery stenosis is an important public health concern internationally, due to it being one of the major causes of ischemic stroke. In this study, we aim to provide a computer-aided diagnosis algorithm capable of automatically distinguishing between Internal Carotid Artery (ICA) stenosis and normal to minimize the labor-intensiveness of stenosis detection. Using Time-of-Flight Magnetic Resonance Angiography (TOF-MRA), a novel deep learning detection model via 3D Squeeze and Excitation Residual Networks (SE-ResNet) is proposed. Pre-processing of TOF-MRA, data augmentation, training of 3D SE-ResNet, and testing using patch-based and patient-based methods with cross-validation is described. The proposed network using a database consisting of 50 normal cases (ICA-N) and 41 stenosis cases (ICA-S) with grade level of above 30% was evaluated. All 41 ICA-S cases were categorized according to the diameter (D_stenosis) of the artery at the site of the most severe stenosis by expert radiologists, whereas percent stenosis was measured by Warfarin-Aspirin Symptomatic Intracranial Disease (WASID) method. The proposed 3D SE-ResNet was further compared with more conventional networks including 3D ResNet and 3D VGG. The results showed the capability to detect stenosis achieving overall Area Under the Curve (AUC) and accuracies of 0.947 and 91.0% for patch-based and 0.884 and 81.0% for patient-based testing, respectively. In addition, the proposed 3D SE-ResNet achieved better performance against conventional 3D ResNet and 3D VGG with improvement rates of 0.053 and 0.095 for patch-based and 0.053 and 0.065 for patient-based testing in terms of AUC, respectively.

Original languageEnglish
Article number9020109
Pages (from-to)43325-43335
Number of pages11
JournalIEEE Access
Publication statusPublished - 2020

Bibliographical note

Funding Information:
Research in the laboratory is supported in part by Grants CA16858 and AI29470 from the NIH and Grant R93-LA086 from the UC University-Wide AIDS Research Program. K.R.C. is the recipient of training support from Training Grant CA09120 and NIH Fellowship AI08994.

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Materials Science(all)
  • Electrical and Electronic Engineering
  • Computer Science(all)


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