PE-Ynet: a novel attention-based multi-task model for pulmonary embolism detection using CT pulmonary angiography (CTPA) scan images.

Autor: Hemalakshmi GR; School of Computing Science and Engineering, Vellore Institute of Technology, Bhopal, Madhya Pradesh, India., Murugappan M; Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, 13133, Doha, Kuwait. m.murugappan@kcst.edu.kw.; Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai, Tamil Nadu, India. m.murugappan@kcst.edu.kw.; Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia. m.murugappan@kcst.edu.kw., Sikkandar MY; Biomedical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majma'ah, Saudi Arabia., Santhi D; Department of Biomedical Engineering, Mepco Schlenk Engineering College, Sivakasi, India., Prakash NB; Department of Electrical and Electronics Engineering, National Engineering College, Kovilpatti, India., Mohanarathinam A; Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, 641021, India.
Jazyk: angličtina
Zdroj: Physical and engineering sciences in medicine [Phys Eng Sci Med] 2024 Sep; Vol. 47 (3), pp. 863-880. Date of Electronic Publication: 2024 Mar 28.
DOI: 10.1007/s13246-024-01410-3
Abstrakt: Pulmonary Embolism (PE) has diverse manifestations with different etiologies such as venous thromboembolism, septic embolism, and paradoxical embolism. In this study, a novel attention-based multi-task model is proposed for PE segmentation and detection from Computed Tomography Pulmonary Angiography (CTPA) images. A Y-Net architecture is used to implement this model, which facilitates segmentation and classification jointly, improving performance and efficiency. It is leveraged with Multi Head Attention (MHA), which allows the model to focus on important regions of the image while suppressing irrelevant information, improving the accuracy of the segmentation and detection tasks. The proposed PE-YNet model is tested with two public datasets, achieving a maximum mean detection and segmentation accuracy of 99.89% and 99.83%, respectively, on the CAD-PE challenge dataset. Similarly, it also achieves a detection accuracy of 99.75% and a segmentation accuracy of 99.81% on the FUMPE dataset. Additionally, sensitivity analysis also shows a high sensitivity of 0.9885 for the localization error ɛ = 0 for the CAD-PE dataset, demonstrating the model's robustness against false predictions compared to state-of-the-art models. Further, this model also exhibits lower inference time, size, and memory usage compared to representative models. An automated PE-YNet tool can assist physicians with PE diagnosis, treatment, and prognosis monitoring in the clinical management of CoVID-19.
(© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)
Databáze: MEDLINE
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