COVID-19 lesion segmentation using convolutional LSTM for self-attention

Aditya Killekar; Kajetan Grodecki; Andrew Lin; Sebastien Cadet; Priscilla McElhinney; Aryabod Razipour; Cato Chan; Barry Pressman; Peter Julien; Peter Chen; Judit Simon; Nitesh Nerlekar; Pál Maurovich-Horvat; Nicola Gaibazzi; Udit Thakur; Elisabetta Mancini; Cecilia Agalbato; Jiro Munechika; Hidenari Matsumoto; Roberto Menè; Damini Dey; Gianfranco Parati; Franco Cernigliaro; Camilla Torlasco; Gianluca Pontone; Piotr Slomka

doi:10.1117/12.2613272

4 April 2022 COVID-19 lesion segmentation using convolutional LSTM for self-attention

Aditya Killekar, Kajetan Grodecki, Andrew Lin, Sebastien Cadet, Priscilla McElhinney, Aryabod Razipour, Cato Chan, Barry Pressman, Peter Julien, Peter Chen, Judit Simon, Nitesh Nerlekar, Pál Maurovich-Horvat, Nicola Gaibazzi, Udit Thakur, Elisabetta Mancini, Cecilia Agalbato, Jiro Munechika, Hidenari Matsumoto, Roberto Menè, Damini Dey, Gianfranco Parati, Franco Cernigliaro, Camilla Torlasco, Gianluca Pontone, Piotr Slomka

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120323P (2022) https://doi.org/10.1117/12.2613272
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

e propose a fast and robust multi-class deep learning framework for segmenting COVID-19 lesions: Ground Glass opacities and High opacities (including consolidations and pleural effusion), from non-contrast CT scans using convolutional Long Short-Term Memory network for self-attention. Our method allows rapid quantification of pneumonia burden from CT with performance equivalent to expert readers. The mean dice score across 5 folds was 0.8776 with a standard deviation of 0.0095. A low standard deviation between results from each fold indicate the models were trained equally good regardless of the training fold. The cumulative per-patient mean dice score (0.8775±0.075) for N=167 patients, after concatenation, is consistent with the results from each of the 5 folds. We obtained excellent Pearson correlation (expert vs. automatic) of 0.9396 (p<0.0001) and 0.9843 (p<0.0001) between ground-glass opacity and high opacity volumes, respectively. Our model outperforms Unet2d (p<0.05) and Unet3d (p<0.05) in segmenting high opacities, has comparable performance with Unet2d in segmenting ground-glass opacities, and significantly outperforms Unet3d (p<0.0001) in segmenting ground-glass opacities. Our model performs faster on CPU and GPU when compared to Unet2d and Unet3d. For same number of input slices, our model consumed 0.83x and 0.26x the memory consumed by Unet2d and Unet3d.

Citation Download Citation

Aditya Killekar, Kajetan Grodecki, Andrew Lin, Sebastien Cadet, Priscilla McElhinney, Aryabod Razipour, Cato Chan, Barry Pressman, Peter Julien, Peter Chen, Judit Simon, Nitesh Nerlekar, Pál Maurovich-Horvat, Nicola Gaibazzi, Udit Thakur, Elisabetta Mancini, Cecilia Agalbato, Jiro Munechika, Hidenari Matsumoto, Roberto Menè, Damini Dey, Gianfranco Parati, Franco Cernigliaro, Camilla Torlasco, Gianluca Pontone, and Piotr Slomka "COVID-19 lesion segmentation using convolutional LSTM for self-attention", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120323P (4 April 2022); https://doi.org/10.1117/12.2613272

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