Cargando…
CNN-Based Quality Assurance for Automatic Segmentation of Breast Cancer in Radiotherapy
Purpose: More and more automatic segmentation tools are being introduced in routine clinical practice. However, physicians need to spend a considerable amount of time in examining the generated contours slice by slice. This greatly reduces the benefit of the tool's automaticity. In order to ove...
Autores principales: | Chen, Xinyuan, Men, Kuo, Chen, Bo, Tang, Yu, Zhang, Tao, Wang, Shulian, Li, Yexiong, Dai, Jianrong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7212344/ https://www.ncbi.nlm.nih.gov/pubmed/32426272 http://dx.doi.org/10.3389/fonc.2020.00524 |
Ejemplares similares
-
Evaluation of Automatic Segmentation Model With Dosimetric Metrics for Radiotherapy of Esophageal Cancer
por: Zhu, Ji, et al.
Publicado: (2020) -
Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images
por: Men, Kuo, et al.
Publicado: (2017) -
A feasibility study on an automated method to generate patient‐specific dose distributions for radiotherapy using deep learning
por: Chen, Xinyuan, et al.
Publicado: (2018) -
MRI-Only Radiotherapy Planning for Nasopharyngeal Carcinoma Using Deep Learning
por: Ma, Xiangyu, et al.
Publicado: (2021) -
Combining distance and anatomical information for deep-learning based dose distribution predictions for nasopharyngeal cancer radiotherapy planning
por: Chen, Xinyuan, et al.
Publicado: (2023)