Biomechanically Constrained Non-rigid MR-TRUS Prostate Registration using Deep Learning based 3D Point Cloud Matching

A non-rigid MR-TRUS image registration framework is proposed for prostate interventions. The registration framework consists of a convolutional neural networks (CNN) for MR prostate segmentation, a CNN for TRUS prostate segmentation and a point-cloud based network for rapid 3D point cloud matching....

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Dettagli Bibliografici
Pubblicato in:Med Image Anal
Autori principali: Fu, Yabo, Lei, Yang, Wang, Tonghe, Patel, Pretesh, Jani, Ashesh B., Mao, Hui, Curran, Walter J., Liu, Tian, Yang, Xiaofeng
Natura: Artigo
Lingua:Inglês
Pubblicazione: 2020
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Article
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC7725979/
https://ncbi.nlm.nih.gov/pubmed/33129147
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.media.2020.101845
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