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QIBDS Net: A Quantum-Inspired Bi-Directional Self-supervised Neural Network Architecture for Automatic Brain MR Image Segmentation

  • Debanjan KonarEmail author
  • Siddhartha Bhattacharyya
  • Bijaya Ketan Panigrahi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)

Abstract

A Quantum-Inspired Bidirectional Self-Organizing Neural Network (QIBDS Net) architecture operated by Quantum-Inspired Multi-level Sigmoidal (QIMUSIG) activation function suitable for fully automatic segmentation of T1-weighted contrast enhanced (T1-CE) MR images, is proposed in real time. The QIBDS Net architecture comprises input, intermediate and output layers of neurons represented as qubits and inter-connected by second order neighborhood based topology. The inter-connections between the intermediate and output layers are effected by means of counter propagation of quantum states without any training or external supervision. Quantum observation is carried out at the end to obtain the segmented tumor from the superposition of quantum states. The proposed self-supervised network architecture has been tested on T1-CE MR images from publicly available data sets and is found to be very efficient while compared with other state of the art techniques.

Keywords

Quantum computing QBDSONN SOFM CNN 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Debanjan Konar
    • 1
    • 3
    Email author
  • Siddhartha Bhattacharyya
    • 2
  • Bijaya Ketan Panigrahi
    • 3
  1. 1.Department of Computer Science and EngineeringSikkim Manipal Institute of Technology, Sikkim Manipal UniversityMajitarIndia
  2. 2.Department of Information TechnologyRCC Institute of Information TechnologyKolkataIndia
  3. 3.Department of Electrical EngineeringIndian Institute of Technology, DelhiNew DelhiIndia

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