Machine Learning for Hepatocellular Carcinoma Segmentation at MRI: Radiology in Training

Alex G. Raman; Craig Jones; Clifford R. Weiss

doi:10.1148/radiol.212386

Machine Learning for Hepatocellular Carcinoma Segmentation at MRI: Radiology in Training

Alex G. Raman
, Craig Jones
, Clifford R. Weiss

School of Medicine

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Pages (from-to)	509-515
Number of pages	7
Journal	RADIOLOGY
Volume	304
Issue number	3
DOIs	https://doi.org/10.1148/radiol.212386
State	Published - Sep 2022

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging

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10.1148/radiol.212386

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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.",

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Machine Learning for Hepatocellular Carcinoma Segmentation at MRI: Radiology in Training

Abstract

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