Published online Feb 7, 2023. doi: 10.3748/wjg.v29.i5.879
Peer-review started: September 25, 2022
First decision: October 18, 2022
Revised: November 26, 2022
Accepted: January 11, 2023
Article in press: January 11, 2023
Published online: February 7, 2023
Small intestinal vascular malformations (angiodysplasias) commonly cause small intestinal bleeding. Therefore, capsule endoscopy has become the primary diagnostic method for angiodysplasias. Nevertheless, manual reading of the entire gastrointestinal tract is a time-consuming heavy workload, which affects the accuracy of diagnosis.
The doctor’s manual reading of the entire gastrointestinal tract is time-consuming, and the heavy workload affects the accuracy of the diagnosis. Also, significant progress has been made in semantic segmentation in the field of deep learning.
This study aimed to assist in the diagnosis and increase the detection rate of angiodysplasias in the small intestine, achieve automatic disease detection, and shorten the capsule endoscopy (CE) reading time.
A convolutional neural network semantic segmentation model with feature fusion automatically recognizes the category of vascular dysplasia under CE and draws the lesion contour, thus improving the efficiency and accuracy of identifying small intestinal vascular malformation lesions, was proposed.
The test set constructed in the study achieved satisfactory results: pixel accuracy was 99%, mean intersection over union was 0.69, negative predictive value was 98.74%, and positive predictive value was 94.27%. The model parameter was 46.38 M, the float calculation was 467.2 G, and the time needed to segment and recognize a picture was 0.6 s.
Constructing a segmentation network based on deep learning to segment and recognize angiodysplasias lesions is an effective and feasible method for diagnosing angiodysplasias lesions.
The model detects the small intestinal malformation lesions in the capsule endoscopy image data and draws the lesion area through segmentation.