摘要
Liver cancer is one of the leading causes of cancer-related mortality worldwide.Magnetic resonance imaging(MRI) is a non-invasive imaging technique that is often used by radiologists for diagnosis and surgical planning.Analysis of a large amount of liver MRI data for each patient limits the radiologist's efficiency and may lead to misdiagnoses.The redundant MRI data,especially from dynamic contrast enhanced(DCE) sequences,is also a bottleneck in transmitting the images via the internet or PACS for remote consultancy in a reasonable amount of time.This study included 25 patients(aged between 20 and 70years) with liver cysts(seven cases),hemangiomas(eight cases),or hepatic cell carcinomas(10 cases).DCE T1 WI MRI was performed for all the patients.The diagnosis reference included typical MRI findings and post-surgery pathology.The methods were as follows:(i) MRI sequence pre-processing based on large vessels variation level set method to remove non-liver parts from MRI images;(ii) human visual model features(luminance,motion,and contour) extraction and fusion;(iii) anomaly-based MRI ranking;and(iv) methods assessment with the 25 patients' DCE MRI data.The prioritization methods applied to the DCE images could automatically assimilate and determine the content of the medical images,identifying the liver cysts,hemangiomas,and carcinomas.The average uniformity between radiologists and prioritization with the proposed method was 0.805,0.838,and0.818 for cysts,hemangiomas,and carcinomas,respectively,which indicates that the proposed method is an efficient method for liver DCE image prioritization.
Liver cancer is one of the leading causes of cancer-related mortality worldwide. Magnetic resonance imaging （MRI） is a non-invasive imaging technique that is often used by radiologists for diagnosis and surgical planning. Analysis of a large amount of liver MRI data for each patient limits the radiologist＇s efficiency and may lead to misdiagnoses. The redundant MRI data, especially from dynamic contrast enhanced （DCE） sequences, is also a bottleneck in transmitting the images via the internet or PACS for remote consultancy in a reasonable amount of time. This study included 25 patients （aged between 20 and 70 years） with liver cysts （seven cases）, hemangiomas （eight cases）, or hepatic cell carcinomas （10 cases）. DCE T1WI MRI was performed for all the patients. The diagnosis reference included typical MRI findings and post-surgery pathology. The methods were as follows： （i） MRI sequence pre-processing based on large vessels variation level set method to remove non-liver parts from MRI images; （ii） human visual model features （luminance, motion, and contour） extraction and fusion; （iii） anomaly-based MRI ranking; and （iv） methods assessment with the 25 patients＇ DCE MRI data. The prioritization methods applied to the DCE images could automatically assimilate and determine the content of the medical images, identifying the liver cysts, hemangiomas, and carcinomas. The average uniformity between radiologists and prioritization with the proposed method was 0.805, 0.838, and 0.818 for cysts, hemangiomas, and carcinomas, respectively, which indicates that the proposed method is an efficient method for liver DCE image prioritization.
