Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study.
Emilia GryskaIsabella Björkman-BurtscherAsgeir Store JakolaTora DunåsJustin SchneidermanRolf A HeckemannPublished in: BMJ open (2022)
Established reproducibility criteria do not sufficiently emphasise description of the preprocessing pipeline. Discrepancies in preprocessing as a result of insufficient reporting are likely to influence segmentation outcomes and hinder clinical utilisation. A detailed description of the whole processing chain, including preprocessing, is thus necessary to obtain stronger evidence of the generalisability of DL-based brain tumour segmentation methods and to facilitate translation of the methods into clinical practice.
Keyphrases
- deep learning
- convolutional neural network
- artificial intelligence
- clinical practice
- resting state
- white matter
- machine learning
- adverse drug
- functional connectivity
- magnetic resonance imaging
- cerebral ischemia
- emergency department
- multiple sclerosis
- metabolic syndrome
- type diabetes
- diffusion weighted imaging
- subarachnoid hemorrhage
- glycemic control