Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images

Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images

Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect...

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Bibliographic Details
Main Authors: Diana Veiga-Canuto, Leonor Cerdà-Alberich, Cinta Sangüesa Nebot, Blanca Martínez de las Heras, Ulrike Pötschger, Michela Gabelloni, José Miguel Carot Sierra, Sabine Taschner-Mandl, Vanessa Düster, Adela Cañete, Ruth Ladenstein, Emanuele Neri, Luis Martí-Bonmatí
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Cancers
Subjects:
tumor segmentation
neuroblastic tumors
deep learning
manual segmentation
automatic segmentation
inter-observer variability
Online Access:https://www.mdpi.com/2072-6694/14/15/3648

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