Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification

Mzoughi, Hiba; Njeh, Ines; Wali, Ali; Slima, Mohamed Ben; BenHamida, Ahmed; Mhiri, Chokri; Mahfoudhe, Kharedine Ben

doi:10.1007/s10278-020-00347-9

Cited by 247 publications

(154 citation statements)

References 37 publications

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“…Zhou et al combined U-Nets of varying depths as UNet++ to improve the medical imaging segmentation performance of the fixed-depth U-Net [ 6 ]. Mzoughi et al designed a 3D CNN layer with small kernels to merge both the local and global contextual information [ 7 ]. Shabanian et al combined 2D U-nets into a 3D breast segmentation model with a suitable projection-fusing approach [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks

Wang

Qin

et al. 2020

PLoS ONE

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Background Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be benefit for the patients in the underdevelopment areas lack of the experienced radiologists. In recent years, with the development of large neural network architectures and medical imaging datasets, deep learning methods have become a methodology of choice for analyzing medical images. The objective of this study was to the construct convolutional neural networks to localize the pneumothorax lesions in chest radiographs. Methods and findings We developed a convolutional neural network, called CheXLocNet, for the segmentation of pneumothorax lesions. The SIIM-ACR Pneumothorax Segmentation dataset was used to train and validate CheXLocNets. The training dataset contained 2079 radiographs with the annotated lesion areas. We trained six CheXLocNets with various hyperparameters. Another 300 annotated radiographs were used to select parameters of these CheXLocNets as the validation set. We determined the optimal parameters by the AP50 (average precision at the intersection over union (IoU) equal to 0.50), a segmentation evaluation metric used by several well-known competitions. Then CheXLocNets were evaluated by a test set (1082 normal radiographs and 290 disease radiographs), based on the classification metrics: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV); segmentation metrics: IoU and Dice score. For the classification, CheXLocNet with best sensitivity produced an AUC of 0.87, sensitivity of 0.78 (95% CI 0.73-0.83), and specificity of 0.78 (95% CI 0.76-0.81). CheXLocNet with best specificity produced an AUC of 0.79, sensitivity of 0.46 (95% CI 0.40-0.52), and specificity of 0.92 (95% CI 0.90-0.94). For the segmentation, CheXLocNet with best sensitivity produced an IoU of 0.69 and Dice score of 0.72. CheXLocNet with best specificity produced an IoU of 0.77 and Dice score of 0.79. We combined them to form an ensemble CheXLocNet. The ensemble CheXLocNet produced an IoU of 0.81 and Dice score of 0.82. Our CheXLocNet succeeded in automatically detecting pneumothorax lesions, without any human guidance. Conclusions In this study, we proposed a deep learning network, called, CheXLocNet, for the automatic segmentation of chest radiographs to detect pneumothorax. Our CheXLocNets generated accurate classification results and high-quality segmentation masks for the pneumothorax at the same time. This technology has the potential to improve healthcare delivery and increase access to chest radiograph expertise for the detection of diseases. Furthermore, the segmentation results can offer comprehensive geometric information of lesions, which can benefit monitoring the sequential development of lesions with high accuracy. Thus, CheXLocNets can be further extended to be a reliable clinical decision support tool. Although we used transfer learning in training CheXLocNet, the parameters of CheXLocNet was still large for the radiograph dataset. Further work is necessary to prune CheXLocNet suitable for the radiograph dataset.

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Section: Introductionmentioning

confidence: 99%

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks

Wang

Qin

et al. 2020

PLoS ONE

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“…Also more recently, researchers have demonstrated achievements of deep learning (DL) in the image segmentation and glioma grades prediction (32)(33)(34)(35)(36)(37). Convolutional neural networks (CNNs) started outperforming other methods on several high-profile image analysis projects.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Radiomics Predicting Tumor Grades and Expression of Multiple Pathologic Biomarkers in Gliomas

et al. 2020

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Background: The grading and pathologic biomarkers of glioma has important guiding significance for the individual treatment. In clinical, it is often necessary to obtain tumor samples through invasive operation for pathological diagnosis. The present study aimed to use conventional machine learning algorithms to predict the tumor grades and pathologic biomarkers on magnetic resonance imaging (MRI) data. Methods: The present study retrospectively collected a dataset of 367 glioma patients, who had pathological reports and underwent MRI scans between October 2013 and March 2019. The radiomic features were extracted from enhanced MRI images, and three frequently-used machine-learning models of LC, Support Vector Machine (SVM), and Random Forests (RF) were built for four predictive tasks: (1) glioma grades, (2) Ki67 expression level, (3) GFAP expression level, and (4) S100 expression level in gliomas. Each sub dataset was split into training and testing sets at a ratio of 4:1. The training sets were used for training and tuning models. The testing sets were used for evaluating models. According to the area under curve (AUC) and accuracy, the best classifier was chosen for each task. Results: The RF algorithm was found to be stable and consistently performed better than Logistic Regression and SVM for all the tasks. The RF classifier on glioma grades achieved a predictive performance (AUC: 0.79, accuracy: 0.81). The RF classifier also achieved a predictive performance on the Ki67 expression (AUC: 0.85, accuracy: 0.80). The AUC and accuracy score for the GFAP classifier were 0.72 and 0.81. The AUC and accuracy score for S100 expression levels are 0.60 and 0.91. Conclusion: The machine-learning based radiomics approach can provide a noninvasive method for the prediction of glioma grades and expression levels of multiple pathologic biomarkers, preoperatively, with favorable predictive accuracy and stability.

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“…Mzoughi et al [ 22 ] applied a DCNN with 11 layers to 3D whole MR images to classify the grade of glioma tumors into LGG or HGG. In their approach, whole 3D volumetric MRI sequences are passed to the DCNN instead of patch extraction from the MR image.…”

Section: Dcnns Application In the Classification Of Brain Cancer Imentioning

confidence: 99%

“…In the recent decade, many researchers have given special attention to Deep Convolutional Neural Networks (DCNNs) as a very powerful machine-vision tool among deep learning techniques [ 12 ]. By applying DCNNs to patients’ X-ray, Computed Tomography (CT), and histopathological images, various types of cancers such as breast [ 13 , 14 ], prostate [ 15 , 16 ], colorectal [ 17 , 18 ], kidney [ 19 , 20 ], and brain [ 21 , 22 ] have been diagnosed in their early stages.…”

Section: Introductionmentioning

confidence: 99%

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey

Shirazi

Fornaciari

McDonnell

et al. 2020

JPM

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In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

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Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification

Cited by 247 publications

References 37 publications

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks

Machine Learning-Based Radiomics Predicting Tumor Grades and Expression of Multiple Pathologic Biomarkers in Gliomas

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey

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