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Related Concept Videos

Endoscopic Procedures V: ERCP01:26

Endoscopic Procedures V: ERCP

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Endoscopic Retrograde Cholangiopancreatography (ERCP) is a diagnostic procedure that combines endoscopy and fluoroscopy to diagnose and treat conditions related to the bile ducts, pancreatic ducts, and gallbladder. This procedure is beneficial for identifying and addressing blockages, gallstones, strictures, and tumors within the biliary or pancreatic systems. ERCP is both diagnostic and therapeutic, offering the ability to visualize and treat identified problems in one session.
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Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
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An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers,...
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Endoscopy is a non-surgical medical technique used to examine a person's internal organs and vessels. This lesson will focus on two types of endoscopic studies: bronchoscopy and thoracoscopy.
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Endoscopic Procedures I: Esophagogastroduodenoscopy01:29

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An Esophagogastroduodenoscopy (EGD) is a diagnostic procedure in which an endoscopist uses a flexible, lighted endoscope to visualize the upper gastrointestinal (GI) tract. The procedure includes visualizing the oropharynx, esophagus, stomach, and the first part of the small intestine, the duodenum.
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Instruments Segmentation in X-ray Fluoroscopic Images for Endoscopic Retrograde Cholangio Pancreatography.

Garance Martin1, Saad El-Madafri1, Aymeric Becq2,3

  • 1Sorbonne Université, CNRS, LIP6, Paris, France.

Studies in Health Technology and Informatics
|May 25, 2022
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This study introduces an improved U-Net model for segmenting endoscopes and guidewires in X-ray images during endoscopic retrograde cholangiopancreatography (ERCP). The method achieved high accuracy, aiding potential future applications in medical procedures.

Keywords:
ERCPU-NetX-Ray Fluoroscopy

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Surgical Navigation

Background:

  • Endoscopic retrograde cholangiopancreatography (ERCP) is a complex procedure requiring precise instrument navigation.
  • Accurate visualization of endoscopes and guidewires in real-time is crucial for successful ERCP outcomes.
  • Current segmentation methods may lack the precision needed for advanced ERCP guidance.

Purpose of the Study:

  • To develop and evaluate an automated method for segmenting endoscopes and guidewires from 2D X-ray fluoroscopic images.
  • To improve the accuracy and reliability of instrument tracking during ERCP procedures.
  • To provide a foundation for AI-driven tools that assist clinicians during ERCP.

Main Methods:

  • An improved U-Net deep learning model was utilized for image segmentation.
  • The model was trained and validated on 2D X-ray fluoroscopic images acquired during ERCP.
  • Quantitative metrics including Dice score and Hausdorff distance were employed for performance evaluation.

Main Results:

  • The proposed method achieved a Dice score of 0.94±0.05 for endoscope segmentation.
  • A Hausdorff distance of 24.26 pixels was obtained for guidewire segmentation, indicating high precision.
  • Preliminary results demonstrate the effectiveness of the deep learning approach for instrument segmentation.

Conclusions:

  • The developed segmentation method shows significant promise for enhancing visualization during ERCP.
  • Accurate segmentation of endoscopes and guidewires can support navigation and potentially reduce procedural risks.
  • Further research and integration into clinical workflows are warranted to fully realize the potential of this technology.