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Updated: Aug 19, 2025

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

The deep radiomic analytics pipeline.

Geoff Currie^1,2, Eric Rohren^1,2

¹School of Dentistry and Medical Sciences, Charles Sturt University, Wagga Wagga, New South Wales, Australia.

Veterinary Radiology & Ultrasound : the Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association

|December 5, 2022

Summary

This summary is machine-generated.

Radiomics extracts imaging features from radiological data. Deep radiomics, using artificial intelligence (AI) in veterinary radiology, offers advanced insights beyond traditional methods for richer image interpretation.

Keywords:

artificial neural network convolutional neural network deep learning deep radiomics radiomics

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Area of Science:

Veterinary Radiology
Medical Imaging
Artificial Intelligence

Background:

Radiomics extracts quantitative features from medical images.
Conventional radiomics uses manual or automated regions of interest.
Artificial intelligence (AI) segmentation is emerging for AI-augmented radiomics.

Purpose of the Study:

To provide a general understanding of radiomics and deep radiomics.
To introduce the concept of deep radiomic feature extraction.
To facilitate discussion on deep radiomics in veterinary imaging.

Main Methods:

Extraction of radiomic features from radiological data.
Utilizing deeper layers of convolutional neural networks for abstract feature generation (deep radiomics).
Application in various veterinary imaging modalities (X-ray, nuclear medicine, CT, ultrasound, MRI).

Main Results:

Radiomics is established in veterinary radiology with conventional features.
AI-augmented radiomics and deep radiomics represent advancements.
Deep radiomic features offer more abstract insights than traditional quantitation.

Conclusions:

Veterinary radiology can benefit from AI, machine learning, and deep learning.
Deep radiomic feature extraction can enrich imaging interpretation.
Understanding deep radiomics is crucial for future development in veterinary imaging.