Machine Learning for Hepatocellular Carcinoma Segmentation at MRI:<i>Radiology</i>In Training
A. Raman, Craig Jones, Clifford R. Weiss
Abstract
A 68-year-old woman with a history of hepatocellular carcinoma underwent conventional transarterial chemoembolization. Manual tumor segmentation on images, which can be used to assess disease progression, is time consuming and may suffer from interobserver reliability issues. The authors present a how-to guide to develop machine learning algorithms for fully automatic segmentation of hepatocellular carcinoma and other tumors for lesion tracking over time.
Topics & Concepts
MedicineHepatocellular carcinomaSegmentationRadiologyCarcinomaLesionArtificial intelligenceSurgeryPathologyInternal medicineComputer scienceRadiomics and Machine Learning in Medical ImagingHepatocellular Carcinoma Treatment and PrognosisBrain Tumor Detection and Classification