Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images

Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images

Objectives. To externally validate and assess the accuracy of a previously trained fully automatic nnU-Net CNN algorithm to identify and segment primary neuroblastoma tumors in MR images in a large children cohort. Methods. An international multicenter, multivendor imaging repository of patients wit...

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Bibliographic Details
Main Authors: Diana Veiga-Canuto, Leonor Cerdà-Alberich, Ana Jiménez-Pastor, José Miguel Carot Sierra, Armando Gomis-Maya, Cinta Sangüesa-Nebot, Matías Fernández-Patón, Blanca Martínez de las Heras, Sabine Taschner-Mandl, Vanessa Düster, Ulrike Pötschger, Thorsten Simon, Emanuele Neri, Ángel Alberich-Bayarri, Adela Cañete, Barbara Hero, Ruth Ladenstein, Luis Martí-Bonmatí
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Cancers
Subjects:
tumor segmentation
independent validation
external validation
neuroblastic tumors
deep learning
automatic segmentation
Online Access:https://www.mdpi.com/2072-6694/15/5/1622

Internet

https://www.mdpi.com/2072-6694/15/5/1622

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