Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools

• Published in: Computers in biology and medicine Vol. 152; p. 106405
• Main Authors: Ranjbarzadeh, Ramin, Caputo, Annalina, Tirkolaee, Erfan Babaee, Jafarzadeh Ghoushchi, Saeid, Bendechache, Malika
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2023; Elsevier Limited
• Subjects: Artificial Intelligence; Biopsy; Brain; Brain - pathology; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain research; Brain tumor; Brain tumors; Cell division; Deep learning; Flow velocity; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Imaging techniques; Information technology; Internal Medicine; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medical prognosis; Metastasis; MRI Modalities; Neuroimaging; Other; Tomography; Tumor classification; Tumor segmentation; Tumors; Brain tumor; MRI Modalities; Artificial intelligence; Tumor segmentation; Tumor classification
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106405

Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients’ lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients survival rates. However, detecting brain tumors in their initial stages is a demanding task and an unmet need. The present study presents a comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images. These AI techniques can be divided into Supervised, Unsupervised, and Deep Learning (DL) methods. Diagnosing and segmenting brain tumors usually begin with Magnetic Resonance Imaging (MRI) on the brain since MRI is a noninvasive imaging technique. Another existing challenge is that the growth of technology is faster than the rate of increase in the number of medical staff who can employ these technologies. It has resulted in an increased risk of diagnostic misinterpretation. Therefore, developing robust automated brain tumor detection techniques has been studied widely over the past years. The current review provides an analysis of the performance of modern methods in this area. Moreover, various image segmentation methods in addition to the recent efforts of researchers are summarized. Finally, the paper discusses open questions and suggests directions for future research. •Reviewing more than 100 scientific research papers from 2015 to 2022.•Investigating brain tumor diagnosis using AI-based models.•Discussing the challenges and future trends of brain tumor segmentation of MRI images.