Aims:
The delineation of outer margins of early Barrett's cancer can be challenging even
for experienced endoscopists. Artificial intelligence (AI) could assist endoscopists
faced with this task. As of date, there is very limited experience in this domain.
In this study, we demonstrate the measure of overlap (Dice coefficient = D) between
highly experienced Barrett endoscopists and an AI system in the delineation of cancer
margins (segmentation task).
Methods:
An AI system with a deep convolutional neural network (CNN) was trained and tested
on high-definition endoscopic images of early Barrett's cancer (n = 33) and normal
Barrett's mucosa (n = 41). The reference standard for the segmentation task were the
manual delineations of tumor margins by three highly experienced Barrett endoscopists.
Training of the AI system included patch generation, patch augmentation and adjustment
of the CNN weights. Then, the segmentation results from patch classification and thresholding
of the class probabilities. Segmentation results were evaluated using the Dice coefficient
(D).
Results:
The Dice coefficient (D) which can range between 0 (no overlap) and 1 (complete overlap)
was computed only for images correctly classified by the AI-system as cancerous. At
a threshold of t = 0.5, a mean value of D = 0.72 was computed.
Conclusions:
AI with CNN performed reasonably well in the segmentation of the tumor region in Barrett's
cancer, at least when compared with expert Barrett's endoscopists. AI holds a lot
of promise as a tool for better visualization of tumor margins but may need further
improvement and enhancement especially in real-time settings.
