Convolutional neural networks for brain tumour segmentation

Bhandari, Abhishta; Koppen, Jarrad; Agzarian, Marc

doi:10.1186/s13244-020-00869-4

Cited by 81 publications

(35 citation statements)

References 33 publications

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“…This has necessitated handcrafted features from the ROI or manual feature selection by clinicians, which is a major drawback of non-deep learning machine learning models. In contrast, this study used a model structure based on a convolutional neural network (CNN), a deep learning algorithm that has repeatedly shown promising ability to process and analyze image data [ 24 , 25 , 26 , 27 , 28 ]. Alongside its strengths in image analysis, CNN also presents other advantages such as automated feature extraction and selection.…”

Section: Discussionmentioning

confidence: 99%

Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients

Yoon

Cheon

Jeong

et al. 2020

Cancers

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This study aimed to investigate the performance of a deep learning-based survival-prediction model, which predicts the overall survival (OS) time of glioblastoma patients who have received surgery followed by concurrent chemoradiotherapy (CCRT). The medical records of glioblastoma patients who had received surgery and CCRT between January 2011 and December 2017 were retrospectively reviewed. Based on our inclusion criteria, 118 patients were selected and semi-randomly allocated to training and test datasets (3:1 ratio, respectively). A convolutional neural network–based deep learning model was trained with magnetic resonance imaging (MRI) data and clinical profiles to predict OS. The MRI was reconstructed by using four pulse sequences (22 slices) and nine images were selected based on the longest slice of glioblastoma by a physician for each pulse sequence. The clinical profiles consist of personal, genetic, and treatment factors. The concordance index (C-index) and integrated area under the curve (iAUC) of the time-dependent area-under-the-curve curves of each model were calculated to evaluate the performance of the survival-prediction models. The model that incorporated clinical and radiomic features showed a higher C-index (0.768 (95% confidence interval (CI): 0.759, 0.776)) and iAUC (0.790 (95% CI: 0.783, 0.797)) than the model using clinical features alone (C-index = 0.693 (95% CI: 0.685, 0.701); iAUC = 0.723 (95% CI: 0.716, 0.731)) and the model using radiomic features alone (C-index = 0.590 (95% CI: 0.579, 0.600); iAUC = 0.614 (95% CI: 0.607, 0.621)). These improvements to the C-indexes and iAUCs were validated using the 1000-times bootstrapping method; all were statistically significant (p < 0.001). This study suggests the synergistic benefits of using both clinical and radiomic parameters. Furthermore, it indicates the potential of multi-parametric deep learning models for the survival prediction of glioblastoma patients.

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Section: Discussionmentioning

confidence: 99%

Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients

Yoon

Cheon

Jeong

et al. 2020

Cancers

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“…Bhandari et al [27], examine the role of CNN's in segment brain tumors by initially taking an informative look at CNN's and conducting dissertation research to discover an instance pipeline for segmentation. Also, Examine the future effectiveness of CNNs by searching for a new field-radionics.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Basir

Shantta

2021

IRA-JAS

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Deep Learning is a growing field of artificial intelligence that has become an operative research topic in a wide range of disciplines. Today we are witnessing the tangible successes of Deep Learning in our daily lives in various applications, including education, manufacturing, transportation, healthcare, military, and automotive, etc.<strong> </strong>Deep Learning is a subfield of Machine Learning that stems from Artificial Neural Networks, where a cascade of layers is employed to progressively extract higher-level features from the raw input and make predictive guesses about new data. This paper will discuss the effect of attribute extraction profoundly inherent in training<strong> </strong>approaches such as Convolutional Neural Networks (CNN). Furthermore, the paper aims to offer a study on Deep Learning techniques and attribute extraction methods that have appeared in the last few years. As the demand increases, considerable research in the attribute extraction assignment has become even more instrumental. Brain tumor characterization and detection will be used as a case study to demonstrate Deep Learning CNN's ability to achieve effective representational learning and tumor characterization.

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“…Convolutional layers use learnable filters (also known as kernels) to create feature maps of an image [21,23]. These kernels are usually small in size, and the layer convolves each filter across the spatial dimensionality of the image to produce a 2D feature map.…”

Section: Convolutional Layermentioning

confidence: 99%

Diagnosis of Medical Images Using Cloud-Deep Learning System

Jacobs

Ali

Sheta

2021

IJET

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Diagnosis of brain tumors is one of the most severe medical problems that affect thousands of people each year in the United States. Manual classification of cancerous tumors through examination of MRI images is a difficult task even for trained professionals. It is an error-prone procedure that is dependent on the experience of the radiologist. Brain tumors, in particular, have a high level of complexity. Therefore, computer-aided diagnosis systems designed to assist with this task are of specific interest for physicians. Accurate detection and classification of brain tumors via magnetic resonance imaging (MRI) examination is a famous approach to analyze MRI images. This paper proposes a method to classify different brain tumors using a Convolutional Neural Network (CNN). We explore the performance of several CNN architectures and examine if decreasing the input image resolution affects the model's accuracy. The dataset used to train the model has initially been 3064 MRI scans. We augmented the data set to 8544 MRI scans to balance the available classes of images. The results show that the design of a suitable CNN architecture can significantly better diagnose medical images. The developed model classification performance was up to 97\% accuracy.

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Convolutional neural networks for brain tumour segmentation

Cited by 81 publications

References 33 publications

Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients

Multi-Parametric Deep Learning Model for Prediction of Overall Survival after Postoperative Concurrent Chemoradiotherapy in Glioblastoma Patients

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Diagnosis of Medical Images Using Cloud-Deep Learning System

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