In Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Radiomics refers to the process of extracting useful imaging features from radiological data. Conventional radiomics like standard uptake value (SUV), intensity histograms or phase images involve hand-crafted (manual) or automated regions of interest (computer generated), however, artificial intelligence (AI) segmentation (AI augmented radiomics) has recently emerged. Radiomic feature extraction extends image insights beyond simply data quantitation and provides additional insights to aid semantic reporting. Deeper layers of a convolutional neural network produce more abstract radiomic features that are referred to as deep radiomics. The application of radiomics in veterinary radiology is already firmly entrenched using hand-crafted and automated computer generated radiomic features in x-ray, nuclear medicine, computed tomography, ultrasound and magnetic resonance imaging. There is opportunity for veterinary radiology to capitalize on advances on AI, machine learning and deep learning to enrich imaging interpretation using deep radiomic feature extraction. This manuscript aims to provide a general understand of radiomics and deep radiomics, and to arm readers with the vernacular to progress discussion and development of deep radiomics in veterinary imaging. This article is protected by copyright. All rights reserved.
Currie Geoff, Rohren Eric
2022-Dec-05
artificial neural network, convolutional neural network, deep learning, deep radiomics, radiomics
