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

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

doi:10.1117/12.2582057

15 February 2021 RV strain classification from 3D CTPA scans using weakly supervised residual attention model

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

Author Affiliations +

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115971P (2021) https://doi.org/10.1117/12.2582057
Event: SPIE Medical Imaging, 2021, Online Only

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.

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Noa Cahan, Edith M. Marom M.D., Shelly Soffer M.D., Yiftach Barash M.D., Eli Konen M.D., Eyal Klang M.D., and Hayit Greenspan "RV strain classification from 3D CTPA scans using weakly supervised residual attention model", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115971P (15 February 2021); https://doi.org/10.1117/12.2582057

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