Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images

PURPOSE: Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep...

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Detalles Bibliográficos
Autores principales: Zhuang, Mingrui, Chen, Zhonghua, Wang, Hongkai, Tang, Hong, He, Jiang, Qin, Bobo, Yang, Yuxin, Jin, Xiaoxian, Yu, Mengzhu, Jin, Baitao, Li, Taijing, Kettunen, Lauri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889459/
https://www.ncbi.nlm.nih.gov/pubmed/36048319
http://dx.doi.org/10.1007/s11548-022-02730-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889459/
https://www.ncbi.nlm.nih.gov/pubmed/36048319
http://dx.doi.org/10.1007/s11548-022-02730-z

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