RV strain classification from 3D CTPA scans using weakly supervised residual attention model

Noa Cahan, Edith M. Marom, Shelly Soffer, Yiftach Barash, Eli Konen, Eyal Klang, Hayit Greenspan

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

Abstract

Pulmonary embolus (PE) refers to obstruction of pulmonary arteries by blood clots. PE accounts for approximately 100,000 deaths per year in the United States alone. The clinical presentation of PE is often nonspecific, making the diagnosis challenging. Thus, rapid and accurate risk stratification is of paramount importance. High-risk PE is caused by right ventricular (RV) dysfunction from acute pressure overload, which in return can help identify which patients require more aggressive therapy. Reconstructed four-chamber views of the heart on chest CT can detect right ventricular enlargement. CT pulmonary angiography (CTPA) is the golden standard in the diagnostic workup of suspected PE. Therefore, it can link between diagnosis and risk stratification strategies. We developed a weakly supervised deep learning algorithm, with an emphasis on novel a attention mechanism, to automatically classify RV strain on CTPA. Our method is a 3D residual block-based model with integrated attention blocks. We evaluated our model on a dataset of CTPAs of emergency department (ED) PE patients. Our results show that attention consistently improves prediction. We infer that unmarked CTPAs can be used for effective RV strain classification. This could be used as a second reader, alerting for high-risk PE patients. To the best of our knowledge, there are no previous deep learning-based studies that attempted to solve this problem.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationComputer-Aided Diagnosis
EditorsMaciej A. Mazurowski, Karen Drukker
PublisherSPIE
ISBN (Electronic)9781510640238
DOIs
StatePublished - 2021
Externally publishedYes
EventMedical Imaging 2021: Computer-Aided Diagnosis - Virtual, Online, United States
Duration: 15 Feb 202119 Feb 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11597
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityVirtual, Online
Period15/02/2119/02/21

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