Direct head-to-head comparison of convolutional long short-term memory and transformer networks for artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary CT angiography

Nipun Manral; Andrew Lin; Caroline Park; Priscilla McElhinney; Aditya Killekar; Matsumoto Hidenari; Jacek Kwiecinski; Konrad Pieszko; Aryabod Razipour; Kajetan Grodecki; Mhairi Doris; Alan C. Kwan; Donghee Han; Keiichiro Kuronuma; Guadalupe Flores Tomasino; Evangelos Tzolos; Aakash Shanbhag; Markus Goeller; Mohamed Marwan; Sebastien Cadet; Stephen Achenbach; Stephen J. Nicholls; Dennis T. Wong; Daniel S. Berman; Marc Dweck; David E. Newby; Michelle C. Williams; Piotr J. Slomka; Damini Dey

doi:10.1117/12.2655556

3 April 2023 Direct head-to-head comparison of convolutional long short-term memory and transformer networks for artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary CT angiography

Nipun Manral, Andrew Lin, Caroline Park, Priscilla McElhinney, Aditya Killekar, Matsumoto Hidenari, Jacek Kwiecinski, Konrad Pieszko, Aryabod Razipour, Kajetan Grodecki, Mhairi Doris, Alan C. Kwan, Donghee Han, Keiichiro Kuronuma, Guadalupe Flores Tomasino, Evangelos Tzolos, Aakash Shanbhag, Markus Goeller, Mohamed Marwan, Sebastien Cadet, Stephen Achenbach, Stephen J. Nicholls, Dennis T. Wong, Daniel S. Berman, Marc Dweck, David E. Newby, Michelle C. Williams, Piotr J. Slomka, Damini Dey

Author Affiliations +

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 1246413 (2023) https://doi.org/10.1117/12.2655556
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Background: compare the performance of 2 novel deep learning networks—convolutional long short-term memory and transformer network—for artificial intelligence-based quantification of plaque volume and stenosis severity from CCTA. Methods: This was an international multicenter study of patients undergoing CCTA at 11 sites. The deep learning (DL) convolutional neural networks were trained to segment coronary plaque in 921 patients (5,045 lesions). The training dataset was further split temporally into training (80%) and internal validation (20%) datasets. The primary DL architecture was a hierarchical convolutional long short- term memory (ConvLSTM) network. This was compared against a TransUNet network, which combines the abilities of Vision Transformer with U-Net, enabling the capture of in-depth localization information while modeling long-range dependencies. Following training and internal validation, the both DL networks were applied to an external validation cohort of 162 patients (1,468 lesions) from the SCOT-HEART trial. Results: In the external validation cohort, agreement between DL and expert reader measurements was stronger when using the ConvLSTM network than with TransUNet, for both per-lesion total plaque volume (ICC 0·953 vs 0.830) and percent diameter stenosis (ICC 0·882 vs 0.735; both p<0.001). The ConvLSTM network showed higher per-cross-section overlap with expert reader segmentations (as measured by the Dice coefficient) compared to TransUnet, for vessel wall (0.947 vs 0.946), lumen (0.93 vs 0.92), and calcified plaque (0.87 vs 0.86; p<0.0001 for all), with similar execution times. Conclusions: In a direct comparison with external validation, the ConvLSTM network yielded higher agreement with expert readers for quantification of total plaque volume and stenosis severity compared to TransUnet, with faster execution times.

Conference Presentation

Citation Download Citation

Nipun Manral, Andrew Lin, Caroline Park, Priscilla McElhinney, Aditya Killekar, Matsumoto Hidenari, Jacek Kwiecinski, Konrad Pieszko, Aryabod Razipour, Kajetan Grodecki, Mhairi Doris, Alan C. Kwan, Donghee Han, Keiichiro Kuronuma, Guadalupe Flores Tomasino, Evangelos Tzolos, Aakash Shanbhag, Markus Goeller, Mohamed Marwan, Sebastien Cadet, Stephen Achenbach, Stephen J. Nicholls, Dennis T. Wong, Daniel S. Berman, Marc Dweck, David E. Newby, Michelle C. Williams, Piotr J. Slomka, and Damini Dey "Direct head-to-head comparison of convolutional long short-term memory and transformer networks for artificial Intelligence-based quantification of atherosclerotic plaque and stenosis from coronary CT angiography", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 1246413 (3 April 2023); https://doi.org/10.1117/12.2655556

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available