Distance-map-supervised feature localisation for MR-TRUS registration-Reference-Cited by-同舟云学术

Distance-map-supervised feature localisation for MR-TRUS registration

Published:2023-09-28 Issue: Volume: Page:1-15
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Ramesh Janjhyam Venkata Naga¹,Sucharitha G.²,Sankardass Veeramalai³,Rani R.⁴,Bhat Nagaraj⁵,Kiran Ajmeera⁶,Rajaram A.⁷

Affiliation:

1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

2. Electronics and Communication Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, Telangana, India

3. Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai, India

4. Department of ECE, Vemu Institute of Technology, Tirupathi-Chittoor Highway, P. Kothakota, Andhra Pradesh

5. Department of ECE, RV College of Engineering, Bengaluru, Karnataka, India

6. Department of Computer Science and Engineering, MLR Institute of Technology, Dundigal, Hyderabad, Telangana, India

7. Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu

Abstract

Although difficult, robust and reliable synchronization of multimodal medical pictures has several practical uses. For instance, in MR-TRUS fusing guided prostate treatments, picture registration between the two modalities is essential. However, due to the significant variety in image appearance and correlation, MR-TRUS picture registration remains a challenging issue. In this research, we suggest employing deep convolutional neural networks (CNN) i.e. three dimensional CNN U-NET (3D-Conv-Net) to develop a resemblance measure for MR-TRUS registration. Finally, for the second-order optimal of the taught measure, we apply a composite optimisation method that searches the solution space for an appropriate starting point. We also use a multi-stage process to improve the optimisation metric.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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