Affiliation:
1. School of Electrical Engineering and Computer Science, Penn State University, University Park, PA 16802, USA
2. Penn State Milton S. Hershey Medical Center, Hershey, PA 17033, USA
Abstract
For patients at risk of developing either lung cancer or colorectal cancer, the identification of suspect lesions in endoscopic video is an important procedure. The physician performs an endoscopic exam by navigating an endoscope through the organ of interest, be it the lungs or intestinal tract, and performs a visual inspection of the endoscopic video stream to identify lesions. Unfortunately, this entails a tedious, error-prone search over a lengthy video sequence. We propose a deep learning architecture that enables the real-time detection and segmentation of lesion regions from endoscopic video, with our experiments focused on autofluorescence bronchoscopy (AFB) for the lungs and colonoscopy for the intestinal tract. Our architecture, dubbed ESFPNet, draws on a pretrained Mix Transformer (MiT) encoder and a decoder structure that incorporates a new Efficient Stage-Wise Feature Pyramid (ESFP) to promote accurate lesion segmentation. In comparison to existing deep learning models, the ESFPNet model gave superior lesion segmentation performance for an AFB dataset. It also produced superior segmentation results for three widely used public colonoscopy databases and nearly the best results for two other public colonoscopy databases. In addition, the lightweight ESFPNet architecture requires fewer model parameters and less computation than other competing models, enabling the real-time analysis of input video frames. Overall, these studies point to the combined superior analysis performance and architectural efficiency of the ESFPNet for endoscopic video analysis. Lastly, additional experiments with the public colonoscopy databases demonstrate the learning ability and generalizability of ESFPNet, implying that the model could be effective for region segmentation in other domains.
Funder
NIH National Cancer Institute
Reference66 articles.
1. Early lung cancer detection;Inage;Clin. Chest Med.,2018
2. Banerjee, A.K. (2024, June 01). Detection of Early Lung Cancer: Autofluorescence Bronchoscopy and Investigational Modalities. Available online: https://helen.drbinhthanh.com/uptodate2/show/4409.html.
3. Automated polyp detection in colonoscopy videos using shape and context information;Tajbakhsh;IEEE Trans. Med. Imaging,2015
4. Deep learning for wireless capsule endoscopy: A systematic review and meta-analysis;Soffer;Gastrointest. Endosc.,2020
5. The promises and challenges of early non-small cell lung cancer detection: Patient perceptions, low-dose CT screening, bronchoscopy and biomarkers;Kalinke;Mol. Oncol.,2021