Dual encoder–decoder-based deep polyp segmentation network for colonoscopy images

Detection of colorectal polyps through colonoscopy is an essential practice in prevention of colorectal cancers. However, the method itself is labor intensive and is subject to human error. With the advent of deep learning-based methodologies, and specifically convolutional neural networks, an oppor...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Scientific reports Jg. 13; H. 1; S. 1183 - 12
Hauptverfasser: Lewis, John, Cha, Young-Jin, Kim, Jongho
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 21.01.2023
Nature Publishing Group
Nature Portfolio
Schlagworte:
639/705/794
692/4020/1394
Colonoscopy
Colorectal cancer
Colorectal carcinoma
Comparative studies
Deep learning
Electric Power Supplies
Female
Generalization, Psychological
Humanities and Social Sciences
Humans
Image processing
Image Processing, Computer-Assisted
Labor, Obstetric
multidisciplinary
Neural networks
Neural Networks, Computer
Polyps
Pregnancy
Science
Science (multidisciplinary)
Segmentation
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Detection of colorectal polyps through colonoscopy is an essential practice in prevention of colorectal cancers. However, the method itself is labor intensive and is subject to human error. With the advent of deep learning-based methodologies, and specifically convolutional neural networks, an opportunity to improve upon the prognosis of potential patients suffering with colorectal cancer has appeared with automated detection and segmentation of polyps. Polyp segmentation is subject to a number of problems such as model overfitting and generalization, poor definition of boundary pixels, as well as the model’s ability to capture the practical range in textures, sizes, and colors. In an effort to address these challenges, we propose a dual encoder–decoder solution named Polyp Segmentation Network (PSNet). Both the dual encoder and decoder were developed by the comprehensive combination of a variety of deep learning modules, including the PS encoder, transformer encoder, PS decoder, enhanced dilated transformer decoder, partial decoder, and merge module. PSNet outperforms state-of-the-art results through an extensive comparative study against 5 existing polyp datasets with respect to both mDice and mIoU at 0.863 and 0.797, respectively. With our new modified polyp dataset we obtain an mDice and mIoU of 0.941 and 0.897 respectively.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-28530-2