Deep learning-based automatic detection, and localization of cancer region using multispectral photoacoustic imaging

Kamal Jnawali; Bhargava Chinni; Virkam Dogra; Navalgund Rao

doi:10.1117/12.2603338

4 April 2022 Deep learning-based automatic detection, and localization of cancer region using multispectral photoacoustic imaging

Kamal Jnawali, Bhargava Chinni, Virkam Dogra, Navalgund Rao

Author Affiliations +

Proceedings Volume 12038, Medical Imaging 2022: Ultrasonic Imaging and Tomography; 120380W (2022) https://doi.org/10.1117/12.2603338
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Photoacoustic imaging (PAI) has the potential to detect cancer in the early stage. PAI is safe due to its non-ionizing radiation properties, which greatly enhance its clinical feasibility in the near future, which provides significant benefits over other imaging techniques like X-ray computed tomography (CT). In this paper, the fully automated 3D deep learning cancer detector is taken to detect and localize the presence of cancer in freshly excised ex-vivo human thyroid and prostate tissue specimens using a three-dimensional (3D) multispectral photoacoustic (MPA) dataset automatically. The model detected and localized the cancer region in a given test MPA image with promising results.

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Kamal Jnawali, Bhargava Chinni, Virkam Dogra, and Navalgund Rao "Deep learning-based automatic detection, and localization of cancer region using multispectral photoacoustic imaging", Proc. SPIE 12038, Medical Imaging 2022: Ultrasonic Imaging and Tomography, 120380W (4 April 2022); https://doi.org/10.1117/12.2603338

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