Work

Deep TPS-PSO: Hybrid Deep Feature Extraction and Global Optimization for Precise 3D MRI Registration

Year: 2025

Type: article

Abstract: This article presents TPS-PSO, a hybrid deformable image registration framework integrating deep learning, non-linear transformation modeling, and global optimization for accurate inter-subject, intra-modality 3D brain MRI alignment. The method combines a 3D ResNet encoder to extract volumetric features, a Thin Plate Spline (TPS) model to capture smooth anatomical deformations, and Particle Swarm Optimization (PSO) to estimate transformation parameters efficiently without relying on gradients. Evaluated on the BraTS 2022 dataset, TPS-PSO achieved state-of-the-art performance with a Dice Similarity Coefficient (DSC) of 85.7%, Mutual Information (MI) of 1.23, Target Registration Error (TRE) of 3.8 mm, HD95 of 6.7 mm, and SSIM of 0.92. Comparative experiments against five recent baselines confirmed consistent improvements. Ablation studies and convergence analysis further validated the contribution of each module and the optimization strategy. The proposed framework generates topologically plausible deformation fields and shows strong potential for clinical and research applications in neuroimaging.

Source: IEEE Open Journal of the Computer Society

DOI: 10.1109/ojcs.2025.3586956

Authors: Gayathri Ramasamy, Tripty Singh, Xiaohui Yuan, Ganesh R Naik

Institutions: Amrita Vishwa Vidyapeetham, University of North Texas, Torrens University Australia

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075