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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Deep-learning-based automatic liver segmentation using computed tomography images in dogs.

Seungyeon Lee1, Genya Shimbo2, Nozomu Yokoyama1

  • 1Laboratory of Veterinary Internal Medicine, Department of Veterinary Clinical Science, Graduate School of Veterinary Medicine, Hokkaido University, Sapporo, Japan.

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|November 6, 2025
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Summary
This summary is machine-generated.

A new deep learning model accurately segments canine livers in CT scans, improving veterinary diagnostics. This automated approach shows high agreement with manual measurements, offering clinical potential.

Keywords:
artificial intelligenceautomatic segmentationcaninecomputed tomographydeep learningdogliver

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Area of Science:

  • Veterinary radiology
  • Medical imaging analysis
  • Artificial intelligence in medicine

Background:

  • Automated segmentation using deep learning (DL) has advanced human medicine.
  • Canine liver segmentation in veterinary medicine remains a challenge.
  • Accurate liver segmentation is crucial for diagnosing and treating canine liver diseases.

Purpose of the Study:

  • To develop and validate a DL model for automated canine liver segmentation.
  • To utilize a 3D U-Net architecture for precise segmentation.
  • To assess the model's performance on canine abdominal CT scans.

Main Methods:

  • A dataset of 221 canine abdominal CT scans was used.
  • The 3D U-Net model was trained and evaluated on two distinct datasets.
  • Experiment 1: Cases without hepatic masses. Experiment 2: Combined cases with and without hepatic masses.

Main Results:

  • Both experiments achieved high segmentation performance.
  • Mean Dice similarity coefficients were 0.926 (Experiment 1) and 0.929 (Experiment 2).
  • Excellent agreement was observed between manual and predicted liver volumes.

Conclusions:

  • The developed 3D U-Net model provides accurate automated liver segmentation in canine CT scans.
  • This approach demonstrates significant potential for clinical application in veterinary medicine.
  • Further validation can enhance diagnostic capabilities for canine liver conditions.