A Review of Brain Tumor Segmentation Using MRIs from 2019 to 2023 (Statistical Information, Key Achievements, and Limitations)

Abstract

Purpose

A brain tumor is defined as any group of atypical cells occupying space in the brain. There are more than 120 types of them. MRI scans are used for brain tumor diagnosis since they are more detailed and three-dimensional. Accurate localization and segmentation of the tumor portion increase the patients' survival rates. To this end, we presented a systematic review of the latest development of brain tumor segmentation from MRI.

Methods

To find related articles, we searched the keywords like "brain tumors" and "segmentation by MRI”. The searches were performed on Elsevier, Springer, Wiley, and the leading conferences in the field of medical image processing. A total of 79 publications dedicated to tumor segmentation from years 2019 to 2023 were selected and categorized into four categories: non-Artificial Intelligence, machine learning, deep learning, and hybrid deep learning methods.

Results

We reviewed the trending techniques of tumor segmentation and provided a unified and integrated overview of the current state-of-the-art. The article dealt with providing the capabilities and shortcomings associated with each approach and the restrictions on using automated medical image segmentation techniques in clinical practice were determined.

Conclusion

In this study, the advancement of brain tumor segmentation by MRI is discussed, focusing more on recent articles. It identified the restrictions of the presented techniques regarding the four mentioned categories, which prevent them from being used in clinical practice. The literature will guide the researchers to become familiar with both the leading techniques and the potential problems that need to be addressed.