11 January 2018 Fully automated detection of breast cancer in screening MRI using convolutional neural networks
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Abstract
Current computer-aided detection (CADe) systems for contrast-enhanced breast MRI rely on both spatial information obtained from the early-phase and temporal information obtained from the late-phase of the contrast enhancement. However, late-phase information might not be available in a screening setting, such as in abbreviated MRI protocols, where acquisition is limited to early-phase scans. We used deep learning to develop a CADe system that exploits the spatial information obtained from the early-phase scans. This system uses three-dimensional (3-D) morphological information in the candidate locations and the symmetry information arising from the enhancement differences of the two breasts. We compared the proposed system to a previously developed system, which uses the full dynamic breast MRI protocol. For training and testing, we used 385 MRI scans, containing 161 malignant lesions. Performance was measured by averaging the sensitivity values between 1/8—eight false positives. In our experiments, the proposed system obtained a significantly ( p = 0.008 ) higher average sensitivity ( 0.6429 ± 0.0537 ) compared with that of the previous CADe system ( 0.5325 ± 0.0547 ). In conclusion, we developed a CADe system that is able to exploit the spatial information obtained from the early-phase scans and can be used in screening programs where abbreviated MRI protocols are used.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Mehmet Ufuk Dalmış, Mehmet Ufuk Dalmış, Suzan Vreemann, Suzan Vreemann, Thijs Kooi, Thijs Kooi, Ritse M. Mann, Ritse M. Mann, Nico Karssemeijer, Nico Karssemeijer, Albert Gubern-Mérida, Albert Gubern-Mérida, } "Fully automated detection of breast cancer in screening MRI using convolutional neural networks," Journal of Medical Imaging 5(1), 014502 (11 January 2018). https://doi.org/10.1117/1.JMI.5.1.014502 . Submission: Received: 24 May 2017; Accepted: 18 December 2017
Received: 24 May 2017; Accepted: 18 December 2017; Published: 11 January 2018
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