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Review
. 2024 Jul 22:12:1392807.
doi: 10.3389/fbioe.2024.1392807. eCollection 2024.

Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey

Zain Ul Abidin  1 , Rizwan Ali Naqvi  1 , Amir Haider  1 , Hyung Seok Kim  1 , Daesik Jeong  2 , Seung Won Lee  3
Affiliations
Review

Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey

Zain Ul Abidin et al. Front Bioeng Biotechnol. .

Abstract

Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.

Keywords: brain tumor segmentation; convolutional neural network; deep learning; medical images; multi-modality analysis; vision transformers.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

FIGURE 1
FIGURE 1
Illustration of several brain MRI modalities (A) T1, (B) T2, (C) T1ce, (D) FLAIR, and (E) ground truth. The yellow, blue, and purple colors in the ground truth represent edema, enhancing, and necrosis, respectively.
FIGURE 2
FIGURE 2
Organization of the DL-based brain tumor segmentation using multi-modality MRI survey.
FIGURE 3
FIGURE 3
Classification of DL-based multi-modal MRI brain tumor segmentation models.
FIGURE 4
FIGURE 4
DL-based multi-modal MRI brain tumor segmentation model publication statistics from 2021 to 2023.
FIGURE 5
FIGURE 5
Statistical analysis of multi-modal brain tumor segmentation datasets used in the DL = based models from 2021 to 2023.
See this image and copyright information in PMC

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