Inverted Cone Convolutional Neural Network For Deboning MRIs
Oliver Palumbo, Dimah Dera, Nidhal Carla Bouaynaya, Hassan M. Fathallah‐Shaykh · 2018
Data plenitude is the bottleneck for data-driven approaches, including neural networks. In particular, Convolutional Neural Networks (CNNs) require an abundant database of training images to achieve a desired high accuracy. Current techniques employed for boosting small datasets are data augmentation and synthetic data generation, which suffer from computational complexity and imprecision compared to original datasets. In this paper, we intercalate prior knowledge based on spatial relation between images in the third dimension by computing the gradient of subsequent images in the dataset to remove extraneous information and highlight subtle variations between images. The approach is coined “Inverted Cone” because the volume of brain images below the level of the eyes is ordered to form an inverted cone geometry. The application explored in this work is deboning, or brain extraction, in brain magnetic resonance imaging (MRI) scans. The difficulty of obtaining ground truth for this application prevents the ability of obtaining a large quantity of training images to train the CNN. We considered a limited dataset of 23 patients with and without malignant glioblastoma. Deboning was performed by employing an optimized CNN architecture with and without the Inverted Cone processing. The classic CNN without prior knowledge achieved a validation accuracy of 77%, while the Inverted Cone CNN model achieved a validation accuracy of 86% in a dataset of 451 brain MRI slices.