Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods
By A. Işın, Cem Direkoǧlu, Melike Šah. 2016
675 citations recorded by the source corpus.
Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.
Subfields: Computer Vision and Pattern Recognition, Neurology
View Paper: https://www.semanticscholar.org/paper/21eac8b9491b92718222fb53b34abd8346fa9d5e
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In Computer Vision and Pattern Recognition
- Ala Al-Fuqaha
- Jing Qin
- Khansa Rasheed
- Mohamed Ghaly
- Giancarlo Fortino
- Junaid Qadir
- Hanwei Chen
- Adeel Razi
In Neurology
Citations in the corpus
- A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network (2021, methodology): “There are several medical imaging techniques used to acquire information about tumors (tumor type, shape, size, location, etc.), which are needed for their diagnosis [2].”
- Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey (2017, background): “Therefore, deep learning is promising in a wide variety of applications including cancer detection and prediction based onmolecular imaging, such as in brain tumor segmentation [8], tumor classification, and survival prediction.”
- Brain tumor detection and classification using machine learning: a comprehensive survey (2021, methodology): “Semi-automated methods are further divided into three forms: initialization, evaluation, and feedback response [113, 114].”
- A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification (2019, background): “In [12-14], the authors introduce a CNN architecture to classify brain tumors.”
- Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “…deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks are widely employed for healthcare applications, i.e., epilepsy seizure prediction [52], segmentation of brain tumor [53], Alzheimer detection [54], genomics [55], and medical prescriptions [56].”