skip to main content
Idioma:

3-D Brain Reconstruction by Hierarchical Shape-Perception Network From a Single Incomplete Image

Hu, Bowen ; Zhan, Choujun ; Tang, Buzhou ; Wang, Bingchuan ; Lei, Baiying ; Wang, Shu-Qiang

IEEE transaction on neural networks and learning systems, 2023-05, Vol.PP, p.1-13

United States: IEEE

Texto completo disponível

Citações Citado por
  • Título:
    3-D Brain Reconstruction by Hierarchical Shape-Perception Network From a Single Incomplete Image
  • Autor: Hu, Bowen ; Zhan, Choujun ; Tang, Buzhou ; Wang, Bingchuan ; Lei, Baiying ; Wang, Shu-Qiang
  • Assuntos: Attention gate block (AGB) ; Decoding ; generative adversarial network (GAN) ; Image reconstruction ; Magnetic resonance imaging ; Pipelines ; point cloud (PC) ; Shape ; shape perception ; Task analysis ; Visualization
  • É parte de: IEEE transaction on neural networks and learning systems, 2023-05, Vol.PP, p.1-13
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: 3-D shape reconstruction is essential in the navigation of minimally invasive and auto robot-guided surgeries whose operating environments are indirect and narrow, and there have been some works that focused on reconstructing the 3-D shape of the surgical organ through limited 2-D information available. However, the lack and incompleteness of such information caused by intraoperative emergencies (such as bleeding) and risk control conditions have not been considered. In this article, a novel hierarchical shape-perception network (HSPN) is proposed to reconstruct the 3-D point clouds (PCs) of specific brains from one single incomplete image with low latency. A branching predictor and several hierarchical attention pipelines are constructed to generate PCs that accurately describe the incomplete images and then complete these PCs with high quality. Meanwhile, attention gate blocks (AGBs) are designed to efficiently aggregate geometric local features of incomplete PCs transmitted by hierarchical attention pipelines and internal features of reconstructing PCs. With the proposed HSPN, 3-D shape perception and completion can be achieved spontaneously. Comprehensive results measured by Chamfer distance (CD) and PC-to-PC error demonstrate that the performance of the proposed HSPN outperforms other competitive methods in terms of qualitative displays, quantitative experiment, and classification evaluation.
  • Editor: United States: IEEE
  • Idioma: Inglês
Links
Voltar para lista de resultados
Resultado  1 AvançarIr para próxima página

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.