Work

Targeting Prostate Biopsy: A Straightforward CNN Solution for 3D MR-Trus Image Registration

Year: 2025

Type: conference-paper

Abstract: Automatic fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for targeted prostate biopsies has significantly enhanced the detection of aggressive cancers. However, obtaining a robust and accurate automatic MR-TRUS registration is challenging because of the sig-nificant differences between the two modalities. This study presents a data-driven deep learning method to predict the rigid transform (6 rotation and translation parameters) between 3D MR and 3D TRUS images. An end-to-end Convolutional neural network is proposed to learn the voxel-level spatial correspondence for multimodal registration tasks. We focus on the model stability and generalization by combining multiple techniques from preprocessing to training strategy. The results show that the proposed network allows accurate prediction of the rigid parameters with a surface registration error (SRE) of 1.81 ± 1.27 mm. The experiments also demonstrate that the proposed method achieves competitive performance compared to other complex rigid networks. The proposed network does not incorporate complexity modules, highlighting the importance of integrating a simpler model design with efficient data preprocessing.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi60581.2025.10981223

Authors: Thi Thao Ho, Clèment Beitone, Jocelyne Troccaz, Sandrine Voros

Institutions: Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, Inserm, VetAgro Sup, Translational Innovation in Medicine and Complexity

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: green

Funder: Agence Nationale de la Recherche