Identifying core MRI sequences for reliable automatic brain metastasis segmentation

Author:

Buchner Josef A,Peeken Jan C,Etzel Lucas,Ezhov Ivan,Mayinger Michael,Christ Sebastian M,Brunner Thomas B,Wittig Andrea,Menze Bjoern H,Zimmer Claus,Meyer Bernhard,Guckenberger Matthias,Andratschke Nicolaus,El Shafie Rami A,Debus Jürgen,Rogers Susanne,Riesterer Oliver,Schulze Katrin,Feldmann Horst J,Blanck Oliver,Zamboglou Constantinos,Ferentinos Konstantinos,Bilger Angelika,Grosu Anca L,Wolff Robert,Kirschke Jan S,Eitz Kerstin A,Combs Stephanie E,Bernhardt Denise,Rueckert Daniel,Piraud Marie,Wiestler Benedikt,Kofler Florian

Funder

Deutsche Forschungsgemeinschaft

Publisher

Elsevier BV

Subject

Radiology, Nuclear Medicine and imaging,Oncology,Hematology

Reference26 articles.

1. Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study;Buchner;Radiother Oncol,2023

2. Kofler F, Wahle J, Ezhov I, Wagner S, Al-Maskari R, Gryska E, et al. Approaching Peak Ground Truth 2022. https://doi.org/10.48550/arXiv.2301.00243.

3. Kofler F, Ezhov I, Isensee F, Balsiger F, Berger C, Koerner M, et al. Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient. Machine Learning for Biomedical Imaging 2021;2:27–71. https://doi.org/10.59275/j.melba.2023-dg1f.

4. Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, et al. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. ArXiv 2018;10:arXiv:1811.02629. https://doi.org/10.48550/ARXIV.1811.02629.

5. Federated learning enables big data for rare cancer boundary detection;Pati;Nat Commun,2022

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