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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2023
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor S. Gimi, Andrzej Krol
ISBN (Electronic)9781510660410
StatePublished - 2023
EventMedical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging - San Diego, United States
Duration: Feb 19 2023Feb 22 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging
Country/TerritoryUnited States
CitySan Diego

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this