3-D multitask deep neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion

Hoang Long Le; Yejin Jeon; Hong Gee Roh; Hyun Jeong Kim; Jin Tae Kwak

doi:10.1117/12.2581011

15 February 2021 3-D multitask deep neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion

Hoang Long Le, Yejin Jeon, Hong Gee Roh, Hyun Jeong Kim, Jin Tae Kwak

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 1159721 (2021) https://doi.org/10.1117/12.2581011
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Acute ischemic stroke (AIS) is not only a common cause of disability but also a leading cause of mortality worldwide. Recent studies have shown that the collateral status could play a vital role in assessing AIS and determining the treatment options for the patients. Herein, we propose a joint regression and ordinal learning approach for AIS, built upon 3-D deep convolutional neural networks, that facilitates an automated and objective collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion (DSC-MRP). DSC-MRP images of 159 AIS subjects and 186 healthy subjects are employed to evaluate the proposed approach. The collateral status is manually assessed in arterial, capillary, early and late venous, and delay phases and served as the ground truth. The proposed method, on average, obtained 0.901 squared correlation coefficient, 0.063 mean absolute error, 0.945 Tanimoto measure, and 0.933 structural similarity index. The quantitative results between AIS and healthy subjects are comparable. Overall, the experimental results suggest that the proposed network could aid in automating the evaluation of collateral status and enhancing the quality and yield of diagnosis of AIS.

Conference Presentation

Citation Download Citation

Hoang Long Le, Yejin Jeon, Hong Gee Roh, Hyun Jeong Kim, and Jin Tae Kwak "3-D multitask deep neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 1159721 (15 February 2021); https://doi.org/10.1117/12.2581011

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