Machine Learning for Hepatocellular Carcinoma Segmentation at MRI: Radiology In Training
Alex G Raman1, Craig Jones1, Clifford R Weiss1
1From the Western University of Health Sciences, College of Osteopathic Medicine of the Pacific, 309 E 2nd St, Pomona, CA 91766 (A.G.R.); Department of Computer Science, Malone Center for Engineering in Healthcare, Johns Hopkins University, Balrimore, Md (C.J.); and Department of Radiology and Radiologic Science, Division of Interventional Radiology, Johns Hopkins Hospital, Baltimore, MD (C.R.W.).
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.
