Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Nadeem, Muhammad Waqas; Ghamdi, Mohammed A. Al; Hussain, Muzammil; Khan, Muhammad Adnan; Khan, Khalid; Almotiri, Sultan H.; Butt, Suhail

doi:10.3390/brainsci10020118

Cited by 176 publications

(85 citation statements)

References 128 publications

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“…The recent development of deep learning methods has drawn much attention for brain image analysis [ 13 , 14 , 15 ]. These methods may provide solutions for predicting molecular subtype gliomas by automatic feature learning.…”

Section: Introductionmentioning

confidence: 99%

Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

Ali

Berger

et al. 2020

Brain Sciences

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Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of 74 . 81 % on 1p/19q codeletion and 81 . 19 % on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

Ali

Berger

et al. 2020

Brain Sciences

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“…The impact of deep learning has been reviewed more specifically in a wide range of medical imaging areas, including abdominal imaging [103] , atherosclerosis imaging [104] , structural and functional brain imaging [105] , [106] , in-vivo cancer imaging [107] , dermatological imaging [108] , endoscopy [109] , mammography [110] , musculoskeletal imaging [111] , nuclear imaging [112] , ophthalmology [113] , pulmonary imaging [114] , thoracic imaging [115] , as well as in radiotherapy [116] , interventional radiology [117] , and radiology in general [118] , [119] , [120] . The massive body of papers on deep learning in virtually all areas of medical imaging has inspired many to write primers [121] , [122] , [123] , guides [124] , [125] , [126] , white papers or roadmaps [127] , [128] , [129] , and other commentaries [130] , [131] , [132] .…”

Section: Deep Learning In Biomedical Imagingmentioning

confidence: 99%

A bird’s-eye view of deep learning in bioimage analysis

Meijering

2020

Computational and Structural Biotechnology Journal

115

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Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird’s-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

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“…Much more work has done in the field of deep and machine learning for the past decade, and in the future, there is a chance that deep learning, machine learning, big data, and information retrieval will remain the most tempting areas for researchers in the medical and engineering fields [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Related Workmentioning

confidence: 99%

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

et al. 2020

Self Cite

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The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

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Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Cited by 176 publications

References 128 publications

Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

A bird’s-eye view of deep learning in bioimage analysis

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

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