Journal of Digital Imaging

, Volume 27, Issue 5, pp 670–678 | Cite as

Semi-Automatic Region-of-Interest Segmentation Based Computer-Aided Diagnosis of Mass Lesions from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Breast Cancer Screening

  • Jacob Levman
  • Ellen Warner
  • Petrina Causer
  • Anne Martel
Brief Communication


Cancer screening with magnetic resonance imaging (MRI) is currently recommended for very high risk women. The high variability in the diagnostic accuracy of radiologists analyzing screening MRI examinations of the breast is due, at least in part, to the large amounts of data acquired. This has motivated substantial research towards the development of computer-aided diagnosis (CAD) systems for breast MRI which can assist in the diagnostic process by acting as a second reader of the examinations. This retrospective study was performed on 184 benign and 49 malignant lesions detected in a prospective MRI screening study of high risk women at Sunnybrook Health Sciences Centre. A method for performing semi-automatic lesion segmentation based on a supervised learning formulation was compared with the enhancement threshold based segmentation method in the context of a computer-aided diagnostic system. The results demonstrate that the proposed method can assist in providing increased separation between malignant and radiologically suspicious benign lesions. Separation between malignant and benign lesions based on margin measures improved from a receiver operating characteristic (ROC) curve area of 0.63 to 0.73 when the proposed segmentation method was compared with the enhancement threshold, representing a statistically significant improvement. Separation between malignant and benign lesions based on dynamic measures improved from a ROC curve area of 0.75 to 0.79 when the proposed segmentation method was compared to the enhancement threshold, also representing a statistically significant improvement. The proposed method has potential as a component of a computer-aided diagnostic system.


Computer-aided diagnosis Magnetic resonance imaging Breast Cancer Supervised learning Pattern recognition 



The MRI data was acquired using funding from the Canadian Breast Cancer Research Alliance. The authors would also like to thank the Canadian Breast Cancer Foundation and the Canadian Institute for Health Research for their financial support for this research project.

Conflicts of Interest

The authors report no conflict of interest.


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Copyright information

© Society for Imaging Informatics in Medicine 2014

Authors and Affiliations

  • Jacob Levman
    • 1
  • Ellen Warner
    • 2
  • Petrina Causer
    • 3
  • Anne Martel
    • 4
  1. 1.Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
  2. 2.Division of Medical Oncology, Department of MedicineSunnybrook Health Sciences CentreTorontoCanada
  3. 3.Medical Imaging DepartmentNorth York General HospitalTorontoCanada
  4. 4.Department of Medical Biophysics, Sunnybrook Health Sciences CentreUniversity of TorontoTorontoCanada

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