The deep radiomic analytics pipeline

Geoff Currie, Eric Rohren

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Radiomics refers to the process of extracting useful imaging features from radiological data. Conventional radiomics like standard uptake value, 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, CT, ultrasound, and MRI. There is an opportunity for veterinary radiology to capitalize on advances in AI, machine learning, and deep learning to enrich imaging interpretation using deep radiomic feature extraction. This manuscript aims to provide a general understanding of radiomics and deep radiomics, and to arm readers with the vernacular to progress discussion and development of deep radiomics in veterinary imaging.

Original languageEnglish (US)
Pages (from-to)889-896
Number of pages8
JournalVeterinary Radiology and Ultrasound
Volume63 Suppl 1
Issue numberS1
DOIs
StatePublished - Dec 2022

Keywords

  • artificial neural network
  • convolutional neural network
  • deep learning
  • deep radiomics
  • radiomics
  • Neural Networks, Computer
  • Magnetic Resonance Imaging
  • Animals
  • Artificial Intelligence
  • Radionuclide Imaging
  • Radiology

ASJC Scopus subject areas

  • veterinary(all)

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