Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study

Table 1 Details of the self-collected dataset

Aspect	Details
Source hospitals	Kiang Wu Hospital, Macao; Xiangyang Central Hospital, Xiangyang, Hubei Province, China
Data collection period	2019-2024
Total images	3313 endoscopic images
Imaging modality	White light endoscopy (majority); narrow band imaging (subset)
Imaging equipment	Olympus EVIS X1 CV-1500 with GIF EZ1500 gastroscopes (Kiang Wu Hospital); Olympus CF-HQ290-I and PENTAX EG29-i10 gastroscopes (Xiangyang Central Hospital)
Disease classes	Normal: 1014 images; esophageal neoplasm: 256 images; esophageal varices: 228 images; GERD: 143 images; gastric neoplasm: 486 images; gastric polyp: 526 images; gastric ulcer: 366 images; gastric varices: 83 images; duodenal ulcer: 211 images
Data acquisition	Images were retrieved from the two hospital databases: (1) Normal images were selected based on chronic superficial gastritis cases exhibiting visually normal appearance on white light endoscopy and no significant pathological findings; and (2) Disease images were identified using international classification of diseases codes under the supervision of gastroenterologists and biomedical engineering postgraduate students
Annotation process	Disease labels were verified by gastroenterologists; Mask annotations were created using Anylabeling by a PhD student and a post-doctoral researcher in biomedical engineering under the supervision of a gastroenterologist; annotations were exported as JSON and converted to binary PNG masks; Masks were independently reviewed by an experienced gastroenterologist
Ethical approval identifiers	Medical Ethics Committee of Xiangyang Central Hospital (No. 2024-145); and Medical Ethics Committee of Kiang Wu Hospital, Macao (No. 2019-005)
Compliance	Conducted in accordance with the Declaration of Helsinki
Pre-processing pipeline	Black borders and text metadata are removed; Images are cropped to resolutions ranging from 268 × 217 pixels to 1545 × 1156 pixels; images are divided into training-and-validation (80%) and test (20%) sets, and further split training-and-validation into training (80%) and validation (20%); multi-class segmentation masks are generated with nine channels: All pixels in normal images are assigned to the “normal” channel. While in disease images, non-lesion/background areas are assigned to the “normal” channel and lesion areas to their respective disease channels

GERD: Gastroesophageal reflux disease; JSON: JavaScript object notation; PNG: Portable network graphics.