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macJNet: weakly-supervised multimodal image deformable registration using joint learning framework and multi-sampling cascaded MIND.
Zhou, Zhiyong; Hong, Ben; Qian, Xusheng; Hu, Jisu; Shen, Minglei; Ji, Jiansong; Dai, Yakang.
Afiliación
  • Zhou Z; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China.
  • Hong B; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China.
  • Qian X; School of Electronic and Optical Engineering, NanJing University of Science and Technology, Nanjing, Jiangsu, China.
  • Hu J; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China.
  • Shen M; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China.
  • Ji J; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, China.
  • Dai Y; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, Jiangsu, China.
Biomed Eng Online ; 22(1): 91, 2023 Sep 19.
Article en En | MEDLINE | ID: mdl-37726780
Deformable multimodal image registration plays a key role in medical image analysis. It remains a challenge to find accurate dense correspondences between multimodal images due to the significant intensity distortion and the large deformation. macJNet is proposed to align the multimodal medical images, which is a weakly-supervised multimodal image deformable registration method using a joint learning framework and multi-sampling cascaded modality independent neighborhood descriptor (macMIND). The joint learning framework consists of a multimodal image registration network and two segmentation networks. The proposed macMIND is a modality-independent image structure descriptor to provide dense correspondence for registration, which incorporates multi-orientation and multi-scale sampling patterns to build self-similarity context. It greatly enhances the representation ability of cross-modal features in the registration network. The semi-supervised segmentation networks generate anatomical labels to provide semantics correspondence for registration, and the registration network helps to improve the performance of multimodal image segmentation by providing the consistency of anatomical labels. 3D CT-MR liver image dataset with 118 samples is built for evaluation, and comprehensive experiments have been conducted to demonstrate that macJNet achieves superior performance over state-of-the-art multi-modality medical image registration methods.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Semántica / Aprendizaje Idioma: En Revista: Biomed Eng Online Asunto de la revista: ENGENHARIA BIOMEDICA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Semántica / Aprendizaje Idioma: En Revista: Biomed Eng Online Asunto de la revista: ENGENHARIA BIOMEDICA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido