Machine Learning for Hepatocellular Carcinoma Segmentation at MRI: Radiology In Training
A 68-year-old woman with a history of hepatocellular carcinoma underwent conventional
transarterial chemoembolization. Manual tumor segmentation on images, which can be …
transarterial chemoembolization. Manual tumor segmentation on images, which can be …
Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks
Objective To assess the performance of convolutional neural networks (CNNs) for
semiautomated segmentation of hepatocellular carcinoma (HCC) tumors on MRI. Methods …
semiautomated segmentation of hepatocellular carcinoma (HCC) tumors on MRI. Methods …
Development of a deep learning-based auto-segmentation algorithm for hepatocellular carcinoma (HCC) and application to predict microvascular invasion of HCC …
S Park, JH Kim, J Kim, W Joseph, D Lee… - Acta …, 2023 - journals.sagepub.com
Background Automatic segmentation has recently been developed to yield objective data.
Prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using …
Prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using …
Deep Learning Combined with Radiologist's Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual‐Phase Magnetic Resonance Images
Y Ye, N Zhang, D Wu, B Huang, X Cai… - BioMed Research …, 2024 - Wiley Online Library
Purpose. Segmentation of hepatocellular carcinoma (HCC) is crucial; however, manual
segmentation is subjective and time‐consuming. Accurate and automatic lesion contouring …
segmentation is subjective and time‐consuming. Accurate and automatic lesion contouring …
Liver tumor segmentation from MR images using 3D fast marching algorithm and single hidden layer feedforward neural network
Objective. Our objective is to develop a computerized scheme for liver tumor segmentation
in MR images. Materials and Methods. Our proposed scheme consists of four main stages …
in MR images. Materials and Methods. Our proposed scheme consists of four main stages …
[PDF][PDF] Automatic liver and tumor segmentation in late-phase MRI using fully convolutional neural networks
Liver and tumor segmentation plays an important role for many liver interventions.
Automation of segmentation steps will bring a substantial improvement to clinical workflows …
Automation of segmentation steps will bring a substantial improvement to clinical workflows …
Improving automatic liver tumor segmentation in late-phase MRI using multi-model training and 3D convolutional neural networks
Automatic liver tumor segmentation can facilitate the planning of liver interventions. For
diagnosis of hepatocellular carcinoma, dynamic contrast-enhanced MRI (DCE-MRI) can …
diagnosis of hepatocellular carcinoma, dynamic contrast-enhanced MRI (DCE-MRI) can …
[PDF][PDF] Learning a prior model for automatic liver lesion segmentation in follow-up CT images
A Militzer, C Tietjen, J Hornegger - 2013 - opus4.kobv.de
Liver tumors that are not surgically removed need to be closely monitored. A common
procedure for their assessment involves acquiring CT images every few months and rating …
procedure for their assessment involves acquiring CT images every few months and rating …
An adaptive method for fully automatic liver segmentation in medical MRI-images
Despite the importance of the liver segmentation in the medical images for efficient
noninvasive diagnosis, few studies found in the literatures for fully automated methods for …
noninvasive diagnosis, few studies found in the literatures for fully automated methods for …
Automated Liver Segmentation for Quantitative MRI Analysis
Dr Cunha is a radiologist and aspiring clinician-scientist in body imaging who has focused
his research on MR imaging of liver diseases and application of deep learning–based …
his research on MR imaging of liver diseases and application of deep learning–based …