V3DQutrit a volumetric medical image segmentation based on 3D qutrit optimized modified tensor ring model

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.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-05-06
DOI
https://doi.org/10.1038/s41598-025-00537-x
Citations
1
Primary Topic
Medical Image Segmentation Techniques
Type
article
Field-Weighted Citation Impact
1.28

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

V3DQutrit a volumetric medical image segmentation based on 3D qutrit optimized modified tensor ring model

Harish Kumar, Pratishtha Verma, Sambit Satpathy, Ala’ M. Al-Zoubi et al.
1 citations
Scientific Reports
Medical Image Segmentation Techniques
1.28
article

V3DQutrit a volumetric medical image segmentation based on 3D qutrit optimized modified tensor ring model

Harish Kumar, Pratishtha Verma, Sambit Satpathy, Ala’ M. Al-Zoubi, Basma S. Alqadi, Jagriti Singh, Osamah Ibrahim Khalaf, Arvinda Kushwaha, Diaa Salama AbdElminaam, Deema Mohammed Alsekait, Dhirendra Kumar Shukla
article en
1 citations

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.

Scientific ReportsVol. 15(1)
National Institute of Technology Kurukshetra (IN), Applied Science Private University (JO), Galgotias University (IN), Benha University (EG), Imam Mohammad ibn Saud Islamic University (SA), Jadara University (JO), Indira Gandhi Delhi Technical University for Women (IN), Nahrain University (IQ)
Princess Nourah Bint Abdulrahman University
Openalex Percentile: Top 21%
Medical Image Segmentation Techniques
1.28
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.