Verma Pratishtha, Kumar Harish, Shukla Dhirendra Kumar, Satpathy Sambit, Alsekait Deema Mohammed, Khalaf Osamah Ibrahim, Alzoubi Ala, Alqadi Basma S, AbdElminaam Diaa Salama, Kushwaha Arvinda, Singh Jagriti
CSE Department, NIT Kurukhetra, Kurukhetra, Hariyana, India.
CSE Department, Galgotias University, Greater Noida, Uttar Pradesh, India.
Sci Rep. 2025 May 6;15(1):15785. doi: 10.1038/s41598-025-00537-x.
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.
本文介绍了3D-QTRNet，这是一种用于体医学图像分割的新型量子启发神经网络。与传统卷积神经网络（CNNs）存在收敛速度慢和复杂度高的问题不同，也与仅限于灰度分割的量子启发神经网络（QINNs）不同，我们的方法利用了三量子比特编码和张量环分解。这些技术提高了分割精度，优化了内存使用，并加速了模型收敛。所提出的模型在BRATS19和脾脏数据集上表现出卓越的性能，在骰子相似性和分割精度方面优于当前最先进的卷积神经网络和量子模型。这项工作弥合了量子计算与医学成像之间的差距，为实际应用提供了一种可扩展的解决方案。
