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Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review

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Abstract

Brain is an amazing organ that controls all activities of a human. Any abnormality in the shape of anatomical regions of the brain needs to be detected as early as possible to reduce the mortality rate. It is also beneficial for treatment planning and therapy. The most crucial task is to isolate abnormal areas from normal tissue regions. To identify abnormalities in the earlier stage, various medical imaging modalities were used by medical practitioners as part of the diagnosis. Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool used for analyzing the internal structures owing to its capability to provide images with high resolution and better contrast for soft tissues. This survey focuses on studies done in brain MRI. Manual segmentation of abnormal tissues is a time-consuming task, and the performance depends on the expert’s efficiency. Hence automating tumor segmentation plays a vital role in medical imaging applications. This study aims to provide a comprehensive survey on recent works developed in brain tumor segmentation. In this paper, a systematic literature review is presented to the reader to understand three policies, namely classical scheme, machine learning strategy, and deep learning methodology meant for tumor segmentation. Our primary goal is to include classical methods like atlas-based strategy and statistical-based models employed for segmenting tumors from brain MRI. Few studies that utilized machine learning approaches for the segmentation and classification of brain structures are also discussed. After that, the study provides an overview of deep learning-based segmentation models for quantitative analysis of brain MRI. Deep learning plays a vital role in the automatic segmentation of brain tissues. Presently deep learning technique outshines traditional statistical methods and machine learning approaches. An effort is made to enclose the literature on patch-based and semantic-based tissue segmentation presented by researchers working in the discipline of medical imaging. The manuscript discusses the basic convolutional neural network architecture, Data Sets, and the existing deep learning techniques for tissue segmentation coupled with classification. This paper also attempts to summarize the current works in Convolutional Neural networks and Autoencoders that assist researchers in seeking future directions. Finally, this article is concluded with possible developments and open challenges in brain tumor segmentation.

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Notes

  1. LBPA40 dataset Available online: https://resource.loni.usc.edu/resources/atlases-downloads/.

  2. IBSR dataset. Available online: https://www.nitrc.org/projects/ibsr.

  3. BrainWeb dataset Available online: https://brainweb.bic.mni.mcgill.ca/.

  4. https://www.med.upenn.edu/cbica/brats2020/data.html

  5. ISLES dataset. Available online: https://www.smir.ch/ISLES/Start2015/

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  1. Department of Computer Science and Engineering, Jyothi Engineering College, Cheruthuruthy, Thrissur, Kerala, India

    Parvathy Jyothi

  2. Department of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India

    A. Robert Singh

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Jyothi, P., Singh, A.R. Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review. Artif Intell Rev 56, 2923–2969 (2023). https://doi.org/10.1007/s10462-022-10245-x

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