Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Vision transformer distillation for enhanced gastrointestinal abnormality recognition in wireless capsule endoscopy images.

Journal of medical imaging (Bellingham, Wash.)·2025

Same author

InCoLoTransNet: An Involution-Convolution and Locality Attention-Aware Transformer for Precise Colorectal Polyp Segmentation in GI Images.

Journal of imaging informatics in medicine·2025

Same author

MLFA-UNet: A multi-level feature assembly UNet for medical image segmentation.

Methods (San Diego, Calif.)·2024

Same author

UViT-Seg: An Efficient ViT and U-Net-Based Framework for Accurate Colorectal Polyp Segmentation in Colonoscopy and WCE Images.

Journal of imaging informatics in medicine·2024

Same author

A Multiscale Polyp Detection Approach for GI Tract Images Based on Improved DenseNet and Single-Shot Multibox Detector.

Diagnostics (Basel, Switzerland)·2023

Same author

Multi-Scale Hybrid Network for Polyp Detection in Wireless Capsule Endoscopy and Colonoscopy Images.

Diagnostics (Basel, Switzerland)·2022

Search research articles

Related Experiment Video

Updated: Aug 5, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Semantic Segmentation of Digestive Abnormalities from WCE Images by Using AttResU-Net Architecture.

Samira Lafraxo¹, Meryem Souaidi¹, Mohamed El Ansari^1,2

¹LabSIV, Department of Computer Science, Faculty of Sciences, Ibn Zohr University, Agadir 80000, Morocco.

Life (Basel, Switzerland)

|March 29, 2023

Summary

Precise polyp segmentation using wireless capsule endoscopy images is crucial for early colorectal cancer detection. The novel AttResU-Net model significantly improves polyp and bleeding detection accuracy, aiding clinical diagnosis.

Keywords:

U-Net WCE attention mechanism colonoscopy deep learning gastrointestinal tract residual block segmentation

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Aug 5, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Pathology

Background:

Colorectal cancer is a leading cause of cancer death globally.
Wireless capsule endoscopy is vital for detecting precancerous digestive diseases.
Manual polyp segmentation from endoscopic images is time-consuming and labor-intensive.

Purpose of the Study:

To develop an automated method for precise polyp segmentation in endoscopic images.
To enhance the accuracy and efficiency of polyp and bleeding detection.
To reduce the diagnostic burden on clinicians.

Main Methods:

An end-to-end 2D attention residual U-Net architecture (AttResU-Net) was proposed.
The model integrates attention mechanisms and residual units into the U-Net framework.
Attention units refine feature salience, while residual blocks enable deeper networks and mitigate vanishing gradients.

Main Results:

The AttResU-Net model achieved high performance on multiple public datasets.
On the MICCAI 2017 WCE dataset, the model attained 99.16% accuracy, 94.91% Dice coefficient, and 90.32% Jaccard index.
The proposed method demonstrated superior performance compared to baseline models and comparable results to existing approaches.

Conclusions:

The AttResU-Net architecture offers a robust and accurate solution for automated polyp segmentation.
This computational technique holds significant clinical value in early colorectal cancer detection and prevention.
The study highlights the potential of deep learning in improving gastrointestinal disease diagnosis.