Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

By Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L. Rubin, Bradley James Erickson. 2017

1,026 citations recorded by the source corpus.

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

Subfields: Computer Vision and Pattern Recognition, Neurology

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “For example the comprehensive review [101],24 covering both medicine and biology and spanning from imaging applications in healthcare to protein-protein interaction and uncertainty quantification; key concepts of deep learning for clinical radiologists [102–111], including radiomics and imaging geno”
Medical Image Segmentation Using Deep Learning: A Survey (2020, background): “[29]provided an overview of current deep learning-based segmentation approaches for quantitative brain MRI images.”
Deep learning based brain tumor segmentation: a survey (2020, methodology): “Deep learning for brain MRI segmentation: state of the art and future directions [20] DI 2017 Review on deep learning based brain MRI segmentation methods”
Deep Learning in Medical Ultrasound Analysis: A Review (2019, background): “Several review articles have been written to date on the application of deep learning to medical image analysis; these articles focus on either the whole ﬁeld of medical image analysis [11–15] or other single-imaging modalities such as MRI [53] and microscopy [54].”
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation (2019, methodology): “It is also common to use hand-tuned data augmentation to increase the number of training examples [2, 55, 57, 63, 65].”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, neutral): “[55] CNN MRI Brain segmentation; the performance is dependent of several factors such as initialization, preprocessing, and post-processing.”
Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI (2018, background): “For manual brain tumour segmentation high intra-and inter-rater variability between 20-30% has been reported [14, 19].”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, background): “In [24], the authors combined the deep oﬀ-the shelf features extracted from a pre-trained VGG19 network with hand-crafted features for determining malignancy of breast lesions in mammography, ultrasound, and MRI.”
Deep Learning Applications in Medical Image Analysis (2018, methodology): “[75], who reviewed various CNN architectures and metrics used in segmentation.”
Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network (2020, background): “Different approaches have been tested on other medical databases, both on MRI images of brain tumors and on tumors from different parts of the human body [19,20].”
Current Applications and Future Impact of Machine Learning in Radiology. (2018, neutral): “Machine learning could be used for quantitative assessment of three-dimensional structures in cross-sectional imaging (62,63).”
Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, background): “Furthermore, collecting valuable information from molecular state and determining disease progression or therapy sensitivity are practical uses of DL that are frequently unidentified by human investigations [23].”