ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video

Qi Chang; Danish Ahmad; Jennifer Toth; Rebecca Bascom; William E. Higgins

doi:10.1117/12.2647897

10 April 2023 ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video

Qi Chang, Danish Ahmad, Jennifer Toth, Rebecca Bascom, William E. Higgins

Author Affiliations +

Proceedings Volume 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1246803 (2023) https://doi.org/10.1117/12.2647897
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Lung cancer tends to be detected at an advanced stage, resulting in a high patient mortality rate. Thus, recent research has focused on early disease detection. Autofluorescence bronchoscopy (AFB) is an effective noninvasive way of detecting early manifestations of lung cancer. Unfortunately, manual inspection of AFB video is extremely tedious and error-prone, while limited effort has been expended toward potentially more robust automatic AFB lesion analysis. We propose a real-time deep-learning architecture dubbed ESFPNet for accurate segmentation and robust detection of bronchial lesions in an AFB video stream. Our approach gives the best segmentation results (mDice = 0.756, mIoU=0.624) on our AFB dataset among recent architectures. Moreover, our model shows promising potential applicability to other domains, as evidenced by its state-of-the-art (SOTA) performance on the CVC-ClinicDB,ETIS-LaribPolypDB datasets, and superior performance on the Kvasir, CVC-ColonDB datasets.

Conference Presentation

Citation Download Citation

Qi Chang, Danish Ahmad, Jennifer Toth, Rebecca Bascom, and William E. Higgins "ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video", Proc. SPIE 12468, Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1246803 (10 April 2023); https://doi.org/10.1117/12.2647897

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