Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions.

Zhang, Yichi; Liao, Qingcheng; Ding, Le; Zhang, Jicong

Zhang, Yichi; Liao, Qingcheng; Ding, Le; Zhang, Jicong.

Afiliación

Zhang Y; School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Liao Q; School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Ding L; School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Zhang J; School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Hefei Innovation Research Institute, Beihang University, Hefei, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beijing, China; Beijing Advanced Innovation Centre for Big Data-Based Precis

Comput Med Imaging Graph ; 99: 102088, 2022 07.

Article en En | MEDLINE | ID: mdl-35780703

Asunto(s)

Imagen por Resonancia Magnética; Redes Neurales de la Computación; Procesamiento de Imagen Asistido por Computador/métodos; Imagenología Tridimensional/métodos

Palabras clave

2.5D Segmentation Methods; Computation-Efficient Learning; Convolutional Neural Network; Medical Image Segmentation

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Imagen por Resonancia Magnética / Redes Neurales de la Computación Idioma: En Revista: Comput Med Imaging Graph Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2022 Tipo del documento: Article País de afiliación: China

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