  Lung cancer tends to be detected at an advanced stage, resulting in a high
patient mortality rate. Thus, much recent research has focused on early disease
detection Bronchoscopy is the procedure of choice for an effective noninvasive
way of detecting early manifestations (bronchial lesions) of lung cancer. In
particular, autofluorescence bronchoscopy (AFB) discriminates the
autofluorescence properties of normal (green) and diseased tissue (reddish
brown) with different colors. Because recent studies show AFB's high
sensitivity in searching lesions, it has become a potentially pivotal method in
bronchoscopic airway exams. 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 (processing throughput of 27 frames/sec) deep-learning architecture
dubbed ESFPNet for accurate segmentation and robust detection of bronchial
lesions in AFB video streams. The architecture features an encoder structure
that exploits pretrained Mix Transformer (MiT) encoders and an efficient
stage-wise feature pyramid (ESFP) decoder structure. Segmentation results from
the AFB airway-exam videos of 20 lung cancer patients indicate that our
approach gives a mean Dice index = 0.756 and an average Intersection of Union =
0.624, results that are superior to those generated by other recent
architectures. Thus, ESFPNet gives the physician a potential tool for confident
real-time lesion segmentation and detection during a live bronchoscopic airway
exam. 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.
