3D Quantum-inspired Self-supervised Tensor Network for Volumetric Segmentation of Brain MR Images
preprintposted on 07.12.2020, 12:21 by Debanjan Konar, Siddhartha Bhattacharyya, Tapan Kumar Gandhi, Bijaya Ketan Panigrahi, Richard Jiang
This paper introduces a novel shallow self-supervised tensor neural network for volumetric segmentation of brain MR images obviating training or supervision. The proposed network is a 3D version of the Quantum-Inspired Self Supervised Neural Network (QIS-Net) architecture and is referred to as 3D Quantum-inspired Self-supervised Tensor Neural Network (3D-QNet). The underlying architecture of 3D-QNet is composed of a trinity of volumetric layers viz. input, intermediate and output layers inter-connected using a 26-connected third-order neighborhood-based topology for voxel-wise processing of 3D MR image data suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation
of tensor decomposition in quantum formalism leads to faster convergence of the network operations to preclude the inherent slow convergence problems faced by the self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3D-QNet is tailored and tested on the BRATS 2019 data set extensively in the experiments carried out. 3D-QNet has achieved promising dice similarity while compared with the intensively supervised convolutional network-based models 3D-UNet, Vox-ResNet, DRINet, and 3D-ESPNet, thus facilitating annotation free semantic segmentation using a self-supervised shallow network.