Convolutional neural network for automated mass segmentation in mammography
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METHODOLOGY
Open Access
Convolutional neural network for automated mass segmentation in mammography Dina Abdelhafiz1,2 , Jinbo Bi1 , Reda Ammar1 , Clifford Yang3 and Sheida Nabavi1* From 8th IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS 2018) Las Vegas, NV, USA. 18–20 October 2018 *Correspondence: [email protected] 1 Department of Computer Science and Engineering, University of Connecticut, 06269 Storrs, CT, USA Full list of author information is available at the end of the article
Abstract Background: Automatic segmentation and localization of lesions in mammogram (MG) images are challenging even with employing advanced methods such as deep learning (DL) methods. We developed a new model based on the architecture of the semantic segmentation U-Net model to precisely segment mass lesions in MG images. The proposed end-to-end convolutional neural network (CNN) based model extracts contextual information by combining low-level and high-level features. We trained the proposed model using huge publicly available databases, (CBIS-DDSM, BCDR-01, and INbreast), and a private database from the University of Connecticut Health Center (UCHC). Results: We compared the performance of the proposed model with those of the state-of-the-art DL models including the fully convolutional network (FCN), SegNet, Dilated-Net, original U-Net, and Faster R-CNN models and the conventional region growing (RG) method. The proposed Vanilla U-Net model outperforms the Faster R-CNN model significantly in terms of the runtime and the Intersection over Union metric (IOU). Training with digitized film-based and fully digitized MG images, the proposed Vanilla U-Net model achieves a mean test accuracy of 92.6%. The proposed model achieves a mean Dice coefficient index (DI) of 0.951 and a mean IOU of 0.909 that show how close the output segments are to the corresponding lesions in the ground truth maps. Data augmentation has been very effective in our experiments resulting in an increase in the mean DI and the mean IOU from 0.922 to 0.951 and 0.856 to 0.909, respectively. Conclusions: The proposed Vanilla U-Net based model can be used for precise segmentation of masses in MG images. This is because the segmentation process (Continued on next page)
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