Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching.
A novel fully automated system is introduced to facilitate lesion detection in dynamic contrast-enhanced, magnetic resonance mammography (DCE-MRM). The system extracts breast regions from pre-contrast images using a cellular neural network, generates normalized maximum intensity-time ratio (nMITR) maps and performs 3D template matching with three layers of 12x12 cells to detect lesions. A breast is considered to be properly segmented when relative overlap >0.85 and misclassification rate <0.10. Sensitivity, false-positive rate per slice and per lesion are used to assess detection performance. The system was tested with a dataset of 2064 breast MR images (344slicesx6 acquisitions over time) from 19 women containing 39 marked lesions. Ninety-seven percent of the breasts were segmented properly and all the lesions were detected correctly (detection sensitivity=100%), however, there were some false-positive detections (31%/lesion, 10%/slice).
Ertaş
Gökhan
G
Biomedical Engineering Institute, Bogaziçi University, Bebek 34342, Istanbul, Turkey. ertasg@boun.edu.tr
Gülçür
H Ozcan
HO
Osman
Onur
O
Uçan
Osman N
ON
Tunaci
Mehtap
M
Dursun
Memduh
M
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D012985:Software Design