Autofluorescence Bronchoscopy Video Analysis for Lesion Frame Detection
This work addresses the need for early detection of lung cancer and squamous cell carcinoma by automating lesion frame detection in AFB videos, making the process more efficient for physicians, though it is incremental as it builds on existing computer vision and machine learning techniques.
The paper tackles the problem of tedious and error-prone manual detection of bronchial lesions in autofluorescence bronchoscopy (AFB) videos by proposing an automated analysis approach that distinguishes informative frames and identifies lesion frames, achieving ≥97% accuracy in both tasks with false positive and negative rates ≤3%.
Because of the significance of bronchial lesions as indicators of early lung cancer and squamous cell carcinoma, a critical need exists for early detection of bronchial lesions. Autofluorescence bronchoscopy (AFB) is a primary modality used for bronchial lesion detection, as it shows high sensitivity to suspicious lesions. The physician, however, must interactively browse a long video stream to locate lesions, making the search exceedingly tedious and error prone. Unfortunately, limited research has explored the use of automated AFB video analysis for efficient lesion detection. We propose a robust automatic AFB analysis approach that distinguishes informative and uninformative AFB video frames in a video. In addition, for the informative frames, we determine the frames containing potential lesions and delineate candidate lesion regions. Our approach draws upon a combination of computer-based image analysis, machine learning, and deep learning. Thus, the analysis of an AFB video stream becomes more tractable. Tests with patient AFB video indicate that $\ge$97\% of frames were correctly labeled as informative or uninformative. In addition, $\ge$97\% of lesion frames were correctly identified, with false positive and false negative rates $\le$3\%.