Deep Learning Based Segmentation of Brain MRI: Systematic Review (from 2018 to 2022) and Meta-Analysis
Keywords:
Deep learning, Meta-analysis, segmentation, dice score, forest plots, publication biasAbstract
Background This paper aims to perform an examination and statistical analysis of deep learning (DL) models utilized in the segmentation of brain tumor MR Images.
Methods The research systematically searched for pertinent research in databases such as PubMed, Science Direct, The Cochrane Library, and Web of Science. The studies related to deep learning (DL) in the context of brain tumor MR image segmentation are included for analysis. Meta-analysis focusing on the dice similarity coefficient (DSC) is conducted to evaluate the segmentation outcomes of these DL models. To categorize the research studies on the basis of sample size and method of segmentation, subgroup analysis is also carried out. Subgroup analysis is important to remove publication bias.
Results Thirty articles are selected from the published research works (n=445) and incorporated into the literature review scope. Eleven cohort studies met the inclusion criteria of the meta-analysis. For the performance of segmented tumors, the average DSC score for the included studies' DLAs is 0.93 (95% CI: 0.88–0.98). However, there is a large amount of variation amongst the papers that were included, and a bias toward publication can also be seen.
Conclusion The accuracy of DLAs used to automate the segmentation of gliomas is high, suggesting that they will be useful in neuroradiology in the future. However, accessible, high-quality public databases and extensive research validation are still required on a large scale.
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