A Texture Neural Network to Predict the Abnormal Brachial Plexus from Routine Magnetic Resonance Imaging

Brachial plexopathy is a form of peripheral neuropathy, which occurs when there is damage to the brachial plexus (BP). However, the diagnosis of breast cancer related BP from radiological imaging is still a great challenge. This paper proposes a texture pattern based convolutional neural network, called TPPNet, to carry out abnormal prediction of BP from multiple routine magnetic resonance image (MRI) pulse sequences, i.e. T2, T1, and T1 post-gadolinium contrast administration. Different from classic CNNs, the input of the proposed TPPNet is multiple texture patterns instead of images. This allows for direct integration of radiomic (i.e. texture) features into the classification models. Beyond conventional radiomic features, we also developed a new family of texture patterns, called triple point patterns (TPPs), to extract huge number of texture patterns as representations of BP' heterogeneity from its MRIs. These texture patterns share the same size and show very stable properties under several geometric transformations. Then, the TPPNet is proposed to carry out the differentiation task of abnormal BP for our study. It has several special characteristics including 1) avoidance of image augmentation, 2) huge number of channels, 3) simple end-to-end architecture, 4) free from the interference of multi-texture-pattern arrangements. Ablation study and comparisons demonstrate that the proposed TPPNet yields outstanding performances with the accuracies of 96.1%, 93.5% and 93.6% over T2, T1 and post-gadolinium sequences which exceed at least 1.3%, 5.3% and 3.4% over state-of-the-art methods for classification of normal vs. abnormal brachial plexus.