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Related Concept Videos

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

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This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
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The neuronal supply to the gastrointestinal (GI) tract is essential for regulating various functions, including digestion, absorption, and movement of food. This intricate network of nerves is known as the enteric nervous system (ENS), often referred to as the "second brain" of the body.
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The human digestive system is an intricate and essential network for nutrient absorption and waste elimination. It encompasses the gastrointestinal (GI) tract and several accessory organs.
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Anatomy of the Intestines01:23

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Although digestion of proteins, carbohydrates, and lipids may begin in the stomach, it is completed in the intestine. The absorption of nutrients, water, and electrolytes from food and drink also occurs in the intestine. The intestines can be divided into two structurally distinct organs—the small and large intestines.
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Related Experiment Video

Updated: Aug 13, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

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U-Net Model with Transfer Learning Model as a Backbone for Segmentation of Gastrointestinal Tract.

Neha Sharma1, Sheifali Gupta1, Deepika Koundal2

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India.

Bioengineering (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

This study introduces a U-Net model for segmenting gastrointestinal (GI) organs, improving radiation therapy accuracy for cancer patients. The novel approach enhances tumor targeting while sparing healthy GI tissues.

Keywords:
GI tractU-Netdeep learningpretrained modelsradiation therapysegmentation

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Computational Biology

Background:

  • Gastrointestinal (GI) tract infections cause significant mortality and morbidity globally.
  • Gastrointestinal diseases affect millions annually, necessitating advanced treatment strategies.
  • Radiation therapy is crucial for GI cancer treatment, but precise tumor targeting is challenging.

Purpose of the Study:

  • To develop an automated segmentation technique for GI tract organs (stomach, small intestine, large intestine).
  • To enhance the accuracy and speed of radiation therapy planning for GI cancer patients.
  • To improve radiation dose delivery to the tumor while minimizing exposure to surrounding healthy GI organs.

Main Methods:

  • A U-Net model was designed from scratch for efficient local feature extraction in medical images.
  • Six transfer learning models (Inception V3, SeResNet50, VGG19, DenseNet121, InceptionResNetV2, EfficientNet B0) were integrated as U-Net backbones.
  • The model's performance was evaluated using standard metrics: model loss, Dice coefficient, and Intersection over Union (IoU).

Main Results:

  • The proposed U-Net model demonstrated superior performance compared to individual transfer learning models.
  • Achieved a model loss of 0.122, a Dice coefficient of 0.8854, and an IoU of 0.8819.
  • The results indicate high accuracy in segmenting GI tract organs.

Conclusions:

  • The developed U-Net model with transfer learning backbones offers a robust solution for GI organ segmentation.
  • This technique can significantly aid radiation oncologists in delivering more precise and effective cancer treatments.
  • The study highlights the potential of deep learning in improving oncological radiotherapy outcomes.