3D-WDA-PMorph: Efficient 3D MRI/TRUS Prostate Registration using Transformer-CNN Network and Wavelet-3D-Depthwise-Attention

R. Leni et al., « Oncologic Outcomes of Incidental Versus Biopsy-diagnosed Grade Group 1 Prostate Cancer: A Multi-institutional Study », European Urology Open Science, vol. 68, p. 10‑17, oct. 2024, https://doi.org/10.1016/j.euros.2024.08.004.

L. S. Ramacciotti et al., « The learning curve for transperineal MRI/TRUS fusion prostate biopsy: A prospective evaluation of a stepwise approach », Urologic Oncology: Seminars and Original Investigations, vol. 43, no 1, p. 64.e1–64.e10, janv. 2025, https://doi.org/10.1016/j.urolonc.2024.08.002.

B. Zitová et J. Flusser, « Image registration methods: a survey », Image and Vision Computing, vol. 21, no 11, p. 977‑1000, oct. 2003, https://doi.org/10.1016/S0262-8856(03)00137-9.

S. Bharati, M. R. H. Mondal, P. Podder, et V. B. S. Prasath, « Deep Learning for Medical Image Registration: A Comprehensive Review », 2022, https://doi.org/10.48550/ARXIV.2204.11341.

H. Ramadan, D. El Bourakadi, A. Yahyaouy, et H. Tairi, « Medical image registration in the era of Transformers: A recent review », Informatics in Medicine Unlocked, vol. 49, p. 101540, janv. 2024, https://doi.org/10.1016/j.imu.2024.101540.

J. Chen, E. C. Frey, Y. He, W. P. Segars, Y. Li, et Y. Du, « TransMorph: Transformer for unsupervised medical image registration », Medical Image Analysis, vol. 82, p. 102615, nov. 2022, https://doi.org/10.1016/j.media.2022.102615.

A. Roy, P. Pramanik, D. Kaplun, S. Antonov, et R. Sarkar, « AWGUNET: Attention-Aided Wavelet Guided U-Net for Nuclei Segmentation in Histopathology Images », 2024, arXiv. https://doi.org/10.48550/ARXIV.2406.08425.

Y. Hu et al., « Label-driven weakly-supervised learning for multimodal deformarle image registration », in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC: IEEE, avr. 2018, p. 1070‑1074. https://doi.org/10.1109/ISBI.2018.8363756.

Y. Hu et al., « Weakly-supervised convolutional neural networks for multimodal image registration », Medical Image Analysis, vol. 49, p. 1‑13, oct. 2018, https://doi.org/10.1016/j.media.2018.07.002.

G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, et A. V. Dalca, « VoxelMorph: A Learning Framework for Deformable Medical Image Registration », IEEE Trans. Med. Imaging, vol. 38, no 8, p. 1788‑1800, août 2019, https://doi.org/10.1109/TMI.2019.2897538.

J. Chen, Y. He, E. C. Frey, Y. Li, et Y. Du, « ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration », 2021, arXiv. https://doi.org/10.48550/ARXIV.2104.06468.

L. Liu, Z. Huang, P. Liò, C.-B. Schönlieb, et A. I. Aviles-Rivero, « PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation », 2022, arXiv. https://doi.org/10.48550/ARXIV.2203.05684.

J. Shi et al., « XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention », in Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, vol. 13436, L. Wang, Q. Dou, P. T. Fletcher, S. Speidel, et S. Li, Éd., in Lecture Notes in Computer Science, vol. 13436. , Cham: Springer Nature Switzerland, 2022, p. 217‑226. https://doi.org/10.1007/978-3-031-16446-0_21.

Y. Zhang, Y. Pei, et H. Zha, « Learning Dual Transformer Network for Diffeomorphic Registration », in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, et C. Essert, Éd., Cham: Springer International Publishing, 2021, p. 129‑138. https://doi.org/10.1007/978-3-030-87202-1_13.

W. Li, K. Gan, L. Yang, et Y. Zhang, « Deformable medical image registration based on wavelet transform and linear attention », Biomedical Signal Processing and Control, vol. 95, p. 106413, sept. 2024, https://doi.org/10.1016/j.bspc.2024.106413.

S.-A. Raza, V. Sanchez, G. Prince, J. P. Clarkson, et N. M. Rajpoot, « Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain », Pattern Recognition, vol. 48, no 7, p. 2119‑2128, juill. 2015, https://doi.org/10.1016/j.patcog.2015.01.027.

G. Hong et Y. Zhang, « Wavelet-based image registration technique for high-resolution remote sensing images », Computers & Geosciences, vol. 34, no 12, p. 1708‑1720, déc. 2008, https://doi.org/10.1016/j.cageo.2008.03.005.

M. Imran et al., « Image registration of in vivo micro-ultrasound and ex vivo pseudo-whole mount histopathology images of the prostate: A proof-of-concept study », Biomedical Signal Processing and Control, vol. 96, p. 106657, oct. 2024, https://doi.org/10.1016/j.bspc.2024.106657.

W. Shao et al., « RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate », Computers in Biology and Medicine, vol. 173, p. 108318, mai 2024, https://doi.org/10.1016/j.compbiomed.2024.108318.

M. Wu, X. He, F. Li, J. Zhu, S. Wang, et P. D. Burstein, « Weakly supervised volumetric prostate registration for MRI-TRUS image driven by signed distance map », Computers in Biology and Medicine, vol. 163, p. 107150, sept. 2023, https://doi.org/10.1016/j.compbiomed.2023.107150.

M. Posiewnik et T. Piotrowski, « Utility of deformable image registration for adaptive prostate cancer treatment. Analysis and comparison of two commercially available algorithms », Zeitschrift für Medizinische Physik, vol. 32, no 3, p. 369‑377, août 2022, https://doi.org/10.1016/j.zemedi.2021.10.001.

Y. Fu et al., « Biomechanically constrained non-rigid MR-TRUS prostate registration using deep learning based 3D point cloud matching », Medical Image Analysis, vol. 67, p. 101845, janv. 2021, https://doi.org/10.1016/j.media.2020.101845.

« Deep learning | Nature Methods ». Consulté le: 18 novembre 2024. [En ligne]. Disponible sur: https://www.nature.com/articles/nmeth.3707

R. Ye, F. Liu, et L. Zhang, « 3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks », 5 août 2018, arXiv: arXiv:1808.01556. https://doi.org/10.48550/arXiv.1808.01556.

Z. Liu et al., « Video Swin Transformer », 24 juin 2021, arXiv: arXiv:2106.13230. Consulté le: 18 novembre 2024. [En ligne]. Disponible sur: http://arxiv.org/abs/2106.13230

F. Milletari, N. Navab, et S.-A. Ahmadi, « V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation », 15 juin 2016, arXiv: arXiv:1606.04797. https://doi.org/10.48550/arXiv.1606.04797.

University College, London, « SmartTarget - A Magnetic Resonance Image to Ultrasound Fusion System for Targeted Prostate Intervention: Biopsy », clinicaltrials.gov, Clinical trial registration NCT02341677, janv. 2019. Consulté le: 9 avril 2025. [En ligne]. Disponible sur: https://clinicaltrials.gov/study/NCT02341677

Comments (0)

No login
gif