Brain Tumor Magnetic Resonance Research Articles

Brain tumor magnetic resonance image segmentation by a multiscale contextual attention module combined with a deep residual UNet (MCA-ResUNet)

Background and Objective. Automatic segmentation of MRI brain tumor area is a key step in the diagnosis and treatment of brain tumor. In recent years, the improved network based on UNet encoding and decoding structure has been widely used in brain tumor segmentation. However, due to continuous convolution and pooling operations, some spatial context information in existing networks will be discontinuous or even missing. It will affect the segmentation accuracy of the model. Therefore, the method proposed in this paper is to alleviate the lack of spatial context information and improve the accuracy of the model. Approach. This paper proposes a context attention module (multiscale contextual attention) to capture and filter out high-level features with spatial context information, which solves the problem of context information loss in feature extraction. The channel attention mechanism is introduced into the decoding structure to realize the fusion of high-level features and low-level features. The standard convolution block in the encoding and decoding structure is replaced by the pre-activated residual block to optimize the network training and improve the network performance. Results. This paper uses two public data sets (BraTs 2017 and BraTs 2019) to evaluate and verify the proposed method. Experimental results show that the proposed method can effectively alleviate the lack of spatial context information, and the segmentation performance is better than other existing methods. Significance. The method improves the segmentation performance of the model. It will assist doctors in making accurate diagnosis and provide reference basis for tumor resection. As a result, the proposed method will reduce the operation risk of patients and the postoperative recurrence rate.

Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application

Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural networks, that belong to the deep learning models, are a subtype of artificial neural networks, which are inspired by the complex structure of the human brain and are often used for image classification tasks. One of the biggest challenges in all deep neural networks is the overfitting issue, which happens when the model performs well on the training data, but fails to make accurate predictions for the new data that is fed into the model. Several regularization methods have been introduced to prevent the overfitting problem. In the research presented in this manuscript, the overfitting challenge was tackled by selecting a proper value for the regularization parameter dropout by utilizing a swarm intelligence approach. Notwithstanding that the swarm algorithms have already been successfully applied to this domain, according to the available literature survey, their potential is still not fully investigated. Finding the optimal value of dropout is a challenging and time-consuming task if it is performed manually. Therefore, this research proposes an automated framework based on the hybridized sine cosine algorithm for tackling this major deep learning issue. The first experiment was conducted over four benchmark datasets: MNIST, CIFAR10, Semeion, and UPS, while the second experiment was performed on the brain tumor magnetic resonance imaging classification task. The obtained experimental results are compared to those generated by several similar approaches. The overall experimental results indicate that the proposed method outperforms other state-of-the-art methods included in the comparative analysis in terms of classification error and accuracy.

Interpretable Model Based on Pyramid Scene Parsing Features for Brain Tumor MRI Image Segmentation.

Due to the black box model nature of convolutional neural networks, computer-aided diagnosis methods based on depth learning are usually poorly interpretable. Therefore, the diagnosis results obtained by these unexplained methods are difficult to gain the trust of patients and doctors, which limits their application in the medical field. To solve this problem, an interpretable depth learning image segmentation framework is proposed in this paper for processing brain tumor magnetic resonance images. A gradient-based class activation mapping method is introduced into the segmentation model based on pyramid structure to visually explain it. The pyramid structure constructs global context information with features after multiple pooling layers to improve image segmentation performance. Therefore, class activation mapping is used to visualize the features concerned by each layer of pyramid structure and realize the interpretation of PSPNet. After training and testing the model on the public dataset BraTS2018, several sets of visualization results were obtained. By analyzing these visualization results, the effectiveness of pyramid structure in brain tumor segmentation task is proved, and some improvements are made to the structure of pyramid model based on the shortcomings of the model shown in the visualization results. In summary, the interpretable brain tumor image segmentation method proposed in this paper can well explain the role of pyramid structure in brain tumor image segmentation, which provides a certain idea for the application of interpretable method in brain tumor segmentation and has certain practical value for the evaluation and optimization of brain tumor segmentation model.

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks.

Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder–decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts.

Compressed SENSE in Pediatric Brain Tumor MR Imaging

PurposeTo compare the image quality, examination time, and total energy release of a standardized pediatric brain tumor magnetic resonance imaging (MRI) protocol performed with and without compressed sensitivity encoding (C-SENSE). Recently introduced as an acceleration technique in MRI, we hypothesized that C‑SENSE would improve image quality, reduce the examination time and radiofrequency-induced energy release compared with conventional examination in a pediatric brain tumor protocol.MethodsThis retrospective study included 22 patients aged 2.33–18.83 years with different brain tumor types who had previously undergone conventional MRI examination and underwent follow-up C‑SENSE examination. Both examinations were conducted with a 3.0-Tesla device and included pre-contrast and post-contrast T1-weighted turbo-field-echo, T2-weighted turbo-spin-echo, and fluid-attenuated inversion recovery sequences. Image quality was assessed in four anatomical regions of interest (tumor area, cerebral cortex, basal ganglia, and posterior fossa) using a 5-point scale. Reader preference between the standard and C‑SENSE images was evaluated. The total examination duration and energy deposit were compared based on scanner log file analysis.ResultsRelative to standard examinations, C‑SENSE examinations were characterized by shorter total examination times (26.1 ± 3.93 vs. 22.18 ± 2.31 min; P = 0.001), reduced total energy deposit (206.0 ± 19.7 vs. 92.3 ± 18.2 J/kg; P < 0.001), and higher image quality (overall P < 0.001).ConclusionC‑SENSE contributes to the improvement of image quality, reduction of scan times and radiofrequency-induced energy release relative to the standard protocol in pediatric brain tumor MRI.

Brain tumor magnetic resonance images classification based machine learning paradigms

Cancer of the nervous system is one of the most common types of cancer in the world and mostly due to presence of a tumour in the brain. The symptoms and severity of the brain tumour depend on its location. The tumour within the brain may develop from nerves, dura (meningioma), pituitary gland (pituitary adenoma), or from the brain tissue itself (glioma). In this study we proposed a feature engineering approach for classification magnetic resonance imaging (MRI) of 3 kinds of most common brain tumour, i.e. glioma, meningioma, pituitary, and no-tumour. Here 5 machine learning classifiers were used, i.e. support vector machine, K-nearest neighbour (KNN), Naive Bayes, Decision Tree, and Ensemble classifier with their paradigms. The handcrafted features such as histogram of oriented gradients, local binary pattern features, and grey level co-occurrence matrix are extracted from the MRI, and the feature fusion technique is adopted to enhance the dimension of feature vector. The Fine KNN outperforms among the classifiers for recognition of 4 kinds of MRI: glioma, meningioma, pituitary, and no tumour, and achieved 91.1% accuracy and 0.95 area under the curve (AUC). The proposed method, i.e. Fine KNN, achieved 91.1% accuracy and 0.96 AUC. Furthermore, this model has the possibility to integrate in low-end devices unlike deep learning, which required a complex system.

A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images

Abnormal growth of brain tissues is the real cause of brain tumor. Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient. The manual segmentation of brain tumor magnetic resonance images (MRIs) takes time and results vary significantly in low-level features. To address this issue, we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network (CNN) for reliable images segmentation by considering the low-level features of MRI. In this model, we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model. To handle the classification process, we have collected a total number of 2043 MRI patients of normal, benign, and malignant tumor. Three model CNN, multi-level CNN, and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors. All the model results are calculated in terms of various numerical values identified as precision (P), recall (R), accuracy (Acc) and f1-score (F1-S). The obtained average results are much better as compared to already existing methods. This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.

GenU-Net++: An Automatic Intracranial Brain Tumors Segmentation Algorithm on 3D Image Series with High Performance

Automatic segmentation of intracranial brain tumors in three-dimensional (3D) image series is critical in screening and diagnosing related diseases. However, there are various challenges in intracranial brain tumor images: (1) Multiple brain tumor categories hold particular pathological features. (2) It is a thorny issue to locate and discern brain tumors from other non-brain regions due to their complicated structure. (3) Traditional segmentation requires a noticeable difference in the brightness of the interest target relative to the background. (4) Brain tumor magnetic resonance images (MRI) have blurred boundaries, similar gray values, and low image contrast. (5) Image information details would be dropped while suppressing noise. Existing methods and algorithms do not perform satisfactorily in overcoming these obstacles mentioned above. Most of them share an inadequate accuracy in brain tumor segmentation. Considering that the image segmentation task is a symmetric process in which downsampling and upsampling are performed sequentially, this paper proposes a segmentation algorithm based on U-Net++, aiming to address the aforementioned problems. This paper uses the BraTS 2018 dataset, which contains MR images of 245 patients. We suggest the generative mask sub-network, which can generate feature maps. This paper also uses the BiCubic interpolation method for upsampling to obtain segmentation results different from U-Net++. Subsequently, pixel-weighted fusion is adopted to fuse the two segmentation results, thereby, improving the robustness and segmentation performance of the model. At the same time, we propose an auto pruning mechanism in terms of the architectural features of U-Net++ itself. This mechanism deactivates the sub-network by zeroing the input. It also automatically prunes GenU-Net++ during the inference process, increasing the inference speed and improving the network performance by preventing overfitting. Our algorithm’s PA, MIoU, P, and R are tested on the validation dataset, reaching 0.9737, 0.9745, 0.9646, and 0.9527, respectively. The experimental results demonstrate that the proposed model outperformed the contrast models. Additionally, we encapsulate the model and develop a corresponding application based on the MacOS platform to make the model further applicable.

Medical Image Segmentation Algorithm Based on Multi-scale Color Wavelet Texture

In order to improve the segmentation accuracy of brain tumor magnetic resonance medical image, a segmentation method of brain tumor magnetic resonance medical image based on multi-scale color wavelet texture features is proposed. The segmentation model of brain tumor magnetic resonance medical image is established, and the motion damage information of brain tumor magnetic resonance medical image is adaptively fused in the ultrasound imaging environment. The medical image information is enhanced by using the motion skeletal muscle block matching technology. According to the suspicious point feature matching method of brain tumor, the fusion detection and processing of brain tumor magnetic resonance medical image are carried out. The multi-scale color wavelet texture feature detection method is used to extract the image features of brain tumor MRI points, and the CT bright spot features are used to analyze the features of brain tumor MRI medical images. Combined with the adaptive neural network training method, the automatic detection of brain tumor magnetic resonance medical image is completed, and the suspected brain tumor points are extracted, so as to realize the segmentation of brain tumor magnetic resonance medical image. Simulation results show that the proposed method can effectively improve the segmentation accuracy of brain tumor MRI medical image, and has high resolution and accuracy for suspicious brain tumor detection.

Feasibility Evaluation of Brain Tumor Magnetic Resonance Imaging Classification Using Convolutional Neural Network Model

Magnetic resonance imaging (MRI) offers information about soft tissues using high-resolution imaging and is widely used for diagnosing conditions such as brain tumors. The purpose of this study was to verify the MRI brain tumor classification function through the convolutional neural network in artificial intelligence. This study used 3,264 MRI datasets which were divided into four classes. To learn artificial intelligence, the training and test datasets were set at 9:1, and 10% of the training dataset was designated as the validation dataset. The convolutional neural network (CNN) was composed of basic CNN and VGG16. Learning was verified through accuracy and loss. The accuracy of the classification function was confirmed through the generated models. There was no overfitting, and the classification function of basic CNN and VGG16 was found to be 67% and 80%, respectively. The MRI brain tumor classification results obtained in this study can be used as the basic data to graft MRI with artificial intelligence.

Automated classification of brain tumors by deep learning-based models on magnetic resonance images using a developed web-based interface

Objective: Primary central nervous system tumors (PCNSTs) compose nearly 3% of newly diagnosed cancers worldwide and are more common in men. The incidence of brain tumors and PCNSTs-related deaths are gradually increasing all over the world. Recently, many studies have focused on automated machine learning (AutoML) algorithms which are developed using deep learning algorithms on medical imaging applications. The main purposes of this study are -to demonstrate the use of artificial intelligence-based techniques to predict medical images of different brain tumors (glioma, meningioma, pituitary adenoma) to provide technical support to radiologists, and -to develop a user-friendly and free web-based software to classify brain tumors for making quick and accurate clinical decisions. &#x0D; &#x0D; Materials and Methods: Open-sourced T1-weighted magnetic resonance brain tumor images were achieved from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, To construct the proposed system which web-based interface and the deep learning-based models, the Keras/Auto-Keras library, which is employed in Python's programming language, is used. Accuracy, sensitivity, specificity, G-mean, F-score, and Matthews correlation coefficient metrics were used for performance evaluations.&#x0D; &#x0D; Results: While 80% (2599 instances) of the dataset was used in the training phase, 20% (465 instances) was employed in the testing phase. All the performance metrics were higher than 98% for the classification of brain tumors on the training data set. Similarly, all the evaluation metrics were higher than 91% except for sensitivity and MCC for meningioma on the testing dataset.&#x0D; &#x0D; Conclusion: The results from the experiment reveal that the proposed software can be used to detect and diagnose three types of brain tumors. This developed web-based software can be accessed freely in both English and Turkish at http://biostatapps.inonu.edu.tr/BTSY/.

Double attention U-Net for brain tumor MR image segmentation

PurposeAutomatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an attention-based nested segmentation network, named DAU-Net. In total, two types of attention mechanisms are introduced to make the U-Net network focus on the key feature regions. The proposed network has a deep supervised encoder–decoder architecture and a redesigned dense skip connection. DAU-Net introduces an attention mechanism between convolutional blocks so that the features extracted at different levels can be merged with a task-related selection.Design/methodology/approachIn the coding layer, the authors designed a channel attention module. It marks the importance of each feature graph in the segmentation task. In the decoding layer, the authors designed a spatial attention module. It marks the importance of different regional features. And by fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.FindingsTo verify the effectiveness of the DAU-Net, experiments were carried out on the BRATS 2018 brain tumor magnetic resonance imaging (MRI) database. The segmentation results show that the proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 89% in the complete tumor, which is an improvement of 8.04 and 4.02%, compared with fully convolutional network (FCN) and U-Net, respectively.Originality/valueThe experimental results show that the proposed method has good performance in the segmentation of brain tumors. The proposed method has potential clinical applicability.

Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint.

Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. In this article, we propose a brain tumor MR image classification method using convolutional dictionary learning with local constraint (CDLLC). Our method integrates the multi-layer dictionary learning into a convolutional neural network (CNN) structure to explore the discriminative information. Encoding a vector on a dictionary can be considered as multiple projections into new spaces, and the obtained coding vector is sparse. Meanwhile, in order to preserve the geometric structure of data and utilize the supervised information, we construct the local constraint of atoms through a supervised k-nearest neighbor graph, so that the discrimination of the obtained dictionary is strong. To solve the proposed problem, an efficient iterative optimization scheme is designed. In the experiment, two clinically relevant multi-class classification tasks on the Cheng and REMBRANDT datasets are designed. The evaluation results demonstrate that our method is effective for brain tumor MR image classification, and it could outperform other comparisons.

Three‐class classification of brain magnetic resonance images using average‐pooling convolutional neural network

AbstractBrain tumor image classification is one of the predominant tasks of brain image processing. The three‐class brain tumor classification becomes a trivial task for researchers as each tumor exhibit distinct characteristics. Existing classification models use deep neural networks and suffer from high computational cost. We have proposed an eight‐layer average‐pooling convolutional neural network to address three‐class brain tumor classification. The proposed model uses three convolution blocks along with a dense layer and a softmax layer. We have utilized N‐adam optimizer with a sparse‐categorical cross‐entropy loss function to improve the learning rate. The proposed model has been evaluated using a dataset consists of 3064 brain tumor magnetic resonance images. The proposed model outperforms state‐of‐the‐art models with 97.42% accuracy and takes lesser computation time than its competitive models.

BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder-Decoder Network.

Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions. These small-scale tumor regions are unable to be identified, the reason being their tiny size and the huge difference between area occupancy by different tumor classes. In previous state-of-the-art neural network models, the biggest problem was that the location information along with spatial details gets lost in deeper layers. To address these problems, we have proposed an encoder–decoder based model named BrainSeg-Net. The Feature Enhancer (FE) block is incorporated into the BrainSeg-Net architecture which extracts the middle-level features from low-level features from the shallow layers and shares them with the dense layers. This feature aggregation helps to achieve better performance of tumor identification. To address the problem associated with imbalance class, we have used a custom-designed loss function. For evaluation of BrainSeg-Net architecture, three benchmark datasets are utilized: BraTS2017, BraTS 2018, and BraTS 2019. Segmentation of Enhancing Core (EC), Whole Tumor (WT), and Tumor Core (TC) is carried out. The proposed architecture have exhibited good improvement when compared with existing baseline and state-of-the-art techniques. The MR brain tumor segmentation by BrainSeg-Net uses enhanced location and spatial features, which performs better than the existing plethora of brain MR image segmentation approaches.

Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement.

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.

Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction.

Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role. Making decisions on critical issues such as cancer prediction utilizing black box models in order to achieve high prediction accuracy but without provision for any sort of explanation for its prediction, accuracy cannot be considered as sufficient and ethnically acceptable. Reasoning and explanation is essential in order to trust these models and support such critical predictions. Nevertheless, the definition and the validation of the quality of a prediction model’s explanation can be considered in general extremely subjective and unclear. In this work, an accurate and interpretable machine learning framework is proposed, for image classification problems able to make high quality explanations. For this task, it is developed a feature extraction and explanation extraction framework, proposing also three basic general conditions which validate the quality of any model’s prediction explanation for any application domain. The feature extraction framework will extract and create transparent and meaningful high level features for images, while the explanation extraction framework will be responsible for creating good explanations relying on these extracted features and the prediction model’s inner function with respect to the proposed conditions. As a case study application, brain tumor magnetic resonance images were utilized for predicting glioma cancer. Our results demonstrate the efficiency of the proposed model since it managed to achieve sufficient prediction accuracy being also interpretable and explainable in simple human terms.

T2-FDL: A robust sparse representation method using adaptive type-2 fuzzy dictionary learning for medical image classification

In this paper, a robust sparse representation for medical image classification is proposed based on the adaptive type-2 fuzzy learning (T2-FDL) system. In the proposed method, sparse coding and dictionary learning processes are executed iteratively until a near-optimal dictionary is obtained. The sparse coding step aiming at finding a combination of dictionary atoms to represent the input data efficiently, and the dictionary learning step rigorously adjusts a minimum set of dictionary items. The two-step operation helps create an adaptive sparse representation algorithm by involving the type-2 fuzzy sets in the design process of image classification. Since the existing image measurements are not made under the same conditions and with the same accuracy, the performance of medical diagnosis is always affected by noise and uncertainty. By introducing an adaptive type-2 fuzzy learning method, a better approximation in an environment with higher degrees of uncertainty and noise is achieved. The experiments are executed over two open-access brain tumor magnetic resonance image databases, REMBRANDT and TCGA-LGG, from The Cancer Imaging Archive (TCIA). The experimental results of a brain tumor classification task show that the proposed T2-FDL method can adequately minimize the negative effects of uncertainty in the input images. The results demonstrate the outperformance of T2-FDL compared to other important classification methods in the literature, in terms of accuracy, specificity, and sensitivity.

基于注意力U-Net的脑肿瘤磁共振图像分割

基于注意力U-Net的脑肿瘤磁共振图像分割