Radiological Physics and Technology

, Volume 2, Issue 2, pp 175–182 | Cite as

Application of an artificial neural network to the computer-aided differentiation of focal liver disease in MR imaging

  • Xuejun Zhang
  • Masayuki Kanematsu
  • Hiroshi Fujita
  • Xiangrong Zhou
  • Takeshi Hara
  • Ryujiro Yokoyama
  • Hiroaki Hoshi


The differentiation of focal liver lesions in magnetic resonance (MR) imaging is primarily based on the intensity and homogeneity of lesions with different imaging sequences. However, these imaging findings are falsely interpreted in some patients because of the complexities involved. Our aim is to establish a computer-aided diagnosis system named LiverANN for classifying the pathologies of focal liver lesions into five categories using the artificial neural network (ANN) technique. On each MR image, a region of interest (ROI) in the focal liver lesion was delineated by a radiologist. The intensity and homogeneity within the ROI were calculated automatically, producing numerical data that were analyzed by feeding them into the LiverANN as inputs. Outputs were the following five pathologic categories of hepatic disease: hepatic cyst, hepatocellular carcinoma, dysplasia in cirrhosis, cavernous hemangioma, and metastasis. Of the 320 MR images obtained from 80 patients (four images per patient) with liver lesions, our LiverANN classified 50 cases of a training set into five types of liver lesions with a training accuracy of 100% and 30 test cases with a testing accuracy of 93%. The experiment demonstrated that our LiverANN, which functions as a computer-aided differentiation tool, can provide radiologists with a second opinion during the radiologic diagnostic procedure.


MR imaging Focal liver disease Differentiation Artificial neural network Computer-aided diagnosis (CAD) 


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Copyright information

© Japanese Society of Radiological Technology and Japan Society of Medical Physics 2009

Authors and Affiliations

  • Xuejun Zhang
    • 1
  • Masayuki Kanematsu
    • 2
  • Hiroshi Fujita
    • 3
  • Xiangrong Zhou
    • 3
  • Takeshi Hara
    • 3
  • Ryujiro Yokoyama
    • 2
  • Hiroaki Hoshi
    • 2
  1. 1.Department of Electronics and Information Engineering, School of Computer, Electronics and InformationGuangxi UniversityNanningPeople’s Republic of China
  2. 2.Department of RadiologyGifu University School of MedicineGifuJapan
  3. 3.Division of Regeneration and Advanced Medical Sciences, Department of Intelligent Image Information, Graduate School of MedicineGifu UniversityGifuJapan

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