Video analysis framework for lesion detection in narrow band imaging bronchoscopy

Vahid Daneshpajooh; Danish Ahmad; Jennifer Toth; Rebecca Bascom; William E. Higgins

doi:10.1117/12.2606054

4 April 2022 Video analysis framework for lesion detection in narrow band imaging bronchoscopy

Vahid Daneshpajooh, Danish Ahmad, Jennifer Toth, Rebecca Bascom, William E. Higgins

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 120360M (2022) https://doi.org/10.1117/12.2606054
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Narrow band imaging (NBI) bronchoscopy enables enhanced visualization of microvascular structures in the mucosal layer of the lungs (airway walls). Such vessels are potential indications of developing cancerous lesions. To find these vascular patterns, the bronchoscope is navigated through the airways, and the physician manually observes potential mucosal vessel structures. We propose an automated video analysis framework based on deep learning and spatial-temporal information in NBI video to find potential cancerous lesions. Using patient data, we demonstrate that our method enables 89% accuracy, 93% sensitivity, and 86% specificity for lesion detection at ~19fps speed. Furthermore, we utilize an upgraded Siamese tracker using kinematic motion modeling jointly with the detection network to isolate abnormalities, achieving 95%/90% accuracy, 90%/74% sensitivity, and 99%/99% specificity, with and without the tracker, respectively.

Conference Presentation

Citation Download Citation

Vahid Daneshpajooh, Danish Ahmad, Jennifer Toth, Rebecca Bascom, and William E. Higgins "Video analysis framework for lesion detection in narrow band imaging bronchoscopy", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 120360M (4 April 2022); https://doi.org/10.1117/12.2606054

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