TY - GEN
T1 - AI based gland detection in barrett s esophagus using optical coherence tomography for capsule endoscopy device
AU - Lee, Jieun
AU - Modi, Vaishnavi K.
AU - Redij, Renisha
AU - Gadam, Srikanth
AU - Gopalakrishnan, Keerthy
AU - Rajagopal, Anjali
AU - Leggett, Cadman L.
AU - Arunachalam, Shivaram P.
N1 - Publisher Copyright:
© 2023 by ASME.
PY - 2023
Y1 - 2023
N2 - Optical coherence tomography (OCT) is an advanced imaging modality to detect Barrett s esophagus (BE) dysplasia, providing widefield, cross-sectional imaging and microscopic resolution. BE dysplasia is characterized under OCT by the presence and number of glandular structures with atypical morphology. Accurate detection and interpretation of BE glands under OCT is essential to detect dysplastic lesions. Object Detection using deep learning has the potential to identify glands from OCT images. We developed a YOLO model to identify the presence of glands in BE tissue. The YOLOv4 object detector was trained on a custom BE dataset of 30 patients with confirmed BE who underwent OCT imaging, of which 222 OCT images included at least one gland. Our model identified glands with a high average precision of 88.79% on the test dataset. We showed that the developed model is robust to rotation, brightness, and blur in images. We have implemented an object detection model to identify glands from OCT images with promising results accurately. This model has the potential to improve the diagnosis and surveillance of BE by eliminating human error and missed dysplastic lesions adaptable for capsule endoscopy applications.
AB - Optical coherence tomography (OCT) is an advanced imaging modality to detect Barrett s esophagus (BE) dysplasia, providing widefield, cross-sectional imaging and microscopic resolution. BE dysplasia is characterized under OCT by the presence and number of glandular structures with atypical morphology. Accurate detection and interpretation of BE glands under OCT is essential to detect dysplastic lesions. Object Detection using deep learning has the potential to identify glands from OCT images. We developed a YOLO model to identify the presence of glands in BE tissue. The YOLOv4 object detector was trained on a custom BE dataset of 30 patients with confirmed BE who underwent OCT imaging, of which 222 OCT images included at least one gland. Our model identified glands with a high average precision of 88.79% on the test dataset. We showed that the developed model is robust to rotation, brightness, and blur in images. We have implemented an object detection model to identify glands from OCT images with promising results accurately. This model has the potential to improve the diagnosis and surveillance of BE by eliminating human error and missed dysplastic lesions adaptable for capsule endoscopy applications.
KW - Barrett s esophagus
KW - Capsule endoscopy
KW - Identification of glands
KW - Object detection
KW - Optical coherence tomography
KW - You Only Look Once (YOLO)
UR - http://www.scopus.com/inward/record.url?scp=85164990640&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85164990640&partnerID=8YFLogxK
U2 - 10.1115/DMD2023-1691
DO - 10.1115/DMD2023-1691
M3 - Conference contribution
AN - SCOPUS:85164990640
T3 - Proceedings of the 2023 Design of Medical Devices Conference, DMD 2023
BT - Proceedings of the 2023 Design of Medical Devices Conference, DMD 2023
PB - American Society of Mechanical Engineers
T2 - 2023 Design of Medical Devices Conference, DMD 2023
Y2 - 17 April 2023 through 21 April 2023
ER -