Performance of a fully automatic lesion detection system for breast DCE-MRI

Anna Vignati, Valentina Giannini, Massimo De Luca, Lia Morra, Diego Persano, Luca A. Carbonaro, Ilaria Bertotto, Laura Martincich, Daniele Regge, Alberto Bert, Francesco Sardanelli

Research output: Contribution to journalArticlepeer-review


Purpose: To describe and test a new fully automatic lesion detection system for breast DCE-MRI. Materials and Methods: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast-enhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (system-detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed. Results: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). Conclusion: The system showed promising results on MR datasets obtained from different scanners producing fat-sat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.

Original languageEnglish
Pages (from-to)1341-1351
Number of pages11
JournalJournal of Magnetic Resonance Imaging
Issue number6
Publication statusPublished - Dec 2011


  • automatic detection
  • breast cancer
  • computer aided detection (CAD)
  • fat-saturation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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