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Computed Tomography

Computed Tomography

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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

Updated: May 1, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Segmentation-based quantitative measurements in renal CT imaging using deep learning.

Konstantinos Koukoutegos^1,2, Richard 's Heeren³, Liesbeth De Wever³

¹KU Leuven, Department of Imaging and Pathology, Division of Medical Physics, Herestraat 49, 3000, Leuven, Belgium. konstantinos.koukoutegos@uzleuven.be.

European Radiology Experimental

|October 9, 2024

Summary

This summary is machine-generated.

Deep learning accurately measures kidneys from CT scans, matching human performance. This AI tool provides fast, precise renal measurements from both contrast-enhanced and noncontrast images, aiding clinical decisions.

Keywords:

Abdomen Artificial intelligence Deep learning Kidney Tomography (x-ray computed)

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Renal quantitative measurements are crucial for assessing kidney function.
Automated kidney measurements from computed tomography (CT) images are needed.

Purpose of the Study:

Develop and validate a deep learning-based method for automated kidney measurements from CT images.
Assess the performance of deep learning networks in segmenting and measuring kidneys.

Main Methods:

Two deep learning networks were trained and validated using datasets of contrast-enhanced and noncontrast CT scans from potential kidney donors.
Segmentation performance was evaluated using the Dice Similarity Coefficient (DSC).
Quantitative measurement accuracy was compared to manual annotations using the Intraclass Correlation Coefficient (ICC).

Main Results:

Deep learning models achieved excellent renal segmentation reliability with DSC scores above 0.92 across various CT datasets.
Volume estimation errors were low, averaging 4-7% mL for both contrast-enhanced and noncontrast scans.
Renal axes measurements demonstrated high accuracy with ICC values greater than 0.90.

Conclusions:

Deep learning networks can automatically derive quantitative renal measurements from both contrast-enhanced and noncontrast CT imaging at a human performance level.
These AI models provide accurate, rapid, and expert-level renal measurements, enhancing clinical decision-making.
Model adaptation requires careful consideration when applied to datasets with unhealthy kidneys.