V3DQutrit a volumetric medical image segmentation based on 3D qutrit optimized modified tensor ring model
Year: 2025
Type: article
Abstract: This paper introduces 3D-QTRNet, a novel quantum-inspired neural network for volumetric medical image segmentation. Unlike conventional CNNs, which suffer from slow convergence and high complexity, and QINNs, which are limited to grayscale segmentation, our approach leverages qutrit encoding and tensor ring decomposition. These techniques improve segmentation accuracy, optimize memory usage, and accelerate model convergence. The proposed model demonstrates superior performance on the BRATS19 and Spleen datasets, outperforming state-of-the-art CNN and quantum models in terms of Dice similarity and segmentation precision. This work bridges the gap between quantum computing and medical imaging, offering a scalable solution for real-world applications.
Source: Scientific Reports
Institutions: National Institute of Technology Kurukshetra, Galgotias University, Galgotias College of Engineering & Technology, Princess Nourah bint Abdulrahman University, Nahrain University, Applied Science Private University, Imam Mohammad ibn Saud Islamic University, Benha University, Jadara University, Indira Gandhi Delhi Technical University for Women
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G5235441952
OpenAlex ID: W4410130535