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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

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Updated: Jun 24, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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A Deep Learning-Based Decision Support System for Cholelithiasis in MRI Data.

Ebru Hasbay1, Caglar Cengizler2, Mahmut Ucar3

  • 1Department of Radiology, Izmir City Hospital, Izmir 35530, Turkey.

Journal of Clinical Medicine
|March 14, 2026
PubMed
Summary

A modified Mask R-CNN deep learning model effectively detects gallstones in magnetic resonance imaging (MRI) scans. This AI tool aids radiologists by automating gallstone identification, improving diagnostic accuracy and efficiency.

Keywords:
MRIR-CNNSqueeze-and-ExcitationU-Netcholelithiasisdeep learninggallbladdersegmentation

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Deep Learning for Radiology
  • Gallbladder Imaging Analysis

Background:

  • Cholelithiasis (gallstones) poses significant health risks if not diagnosed promptly.
  • Advancements in deep learning enable automated detection of gallstones in MRI.
  • AI can reduce the time and resources needed for gallbladder evaluations.

Purpose of the Study:

  • To develop an AI support system with a graphical user interface for detecting gallstones in gallbladder MRI.
  • To reduce manual effort and time in identifying gallstones.
  • To automatically locate and label gallbladders in T2-weighted axial MR images for gallstone detection.

Main Methods:

  • Modified Mask Region Based Convolutional Neural Network (Mask R-CNN) for instance segmentation.
  • Training and evaluation on 788 axial MR images, with radiologist-labeled segmentation.
  • Focus on a single optimal slice for automated analysis to support radiologists.

Main Results:

  • The modified Mask R-CNN model achieved up to 0.89 accuracy in gallstone detection.
  • Squeeze and excitation (SE) modification improved classification accuracy.
  • Image-level stone detection showed improved accuracy, precision, and specificity.

Conclusions:

  • The modified Mask R-CNN model demonstrates significant potential for clinical application in gallstone detection.
  • Automated analysis of gallbladder MRI using AI can support radiological diagnosis.
  • The developed system offers a practical approach to improving diagnostic efficiency.