scholarly journals QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Mohsen Ahmadi ◽  
Abbas Sharifi ◽  
Shayan Hassantabar ◽  
Saman Enayati
Keyword(s):  
Neural Network ◽  
Brain Tumors ◽  
Matched Filter ◽  
Conditional Random Field ◽  
Brain Mri ◽  
Radiotherapy Planning ◽  
Average Error ◽  
Tumor Segmentation ◽  
Spiking Neural Network ◽  
Filter Technique

Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many cases, the presented methods’ reliability is at a high level, and such systems are used directly. In recent decades, several methods of segmentation of various images, such as MRI, CT, and PET, have been proposed for brain tumors. Advanced brain tumor segmentation has been a challenging issue in the scientific community. The reason for this is the existence of various tumor dimensions with disproportionate boundaries in medical imaging. This research provides an optimized MRI segmentation method to diagnose tumors. It first offers a preprocessing approach to reduce noise with a new method called Quantum Matched-Filter Technique (QMFT). Then, the deep spiking neural network (DSNN) is implemented for segmentation using the conditional random field structure. However, a new algorithm called the Quantum Artificial Immune System (QAIS) is used in its SoftMax layer due to its slowness and nonsegmentation and the identification of suitable features for selection and extraction. The proposed approach, called QAIS-DSNN, has a high ability to segment and distinguish brain tumors from MRI images. The simulation results using the BraTS2018 dataset show that the accuracy of the proposed approach is 98.21%, average error-squared rate is 0.006, signal-to-noise ratio is 97.79 dB, and lesion structure criteria including the tumor nucleus are 80.15%. The improved tumor is 74.50%, and the entire tumor is 91.92%, which shows a functional advantage over similar previous methods. Also, the execution time of this method is 2.58 seconds.

Segmentation of Gliomas Based on a Double-Pathway Residual Convolution Neural Network Using Multi-Modality Information

2020 ◽  
Vol 10 (11) ◽  
pp. 2784-2794
Author(s):  
Mingyuan Pan ◽  
Yonghong Shi ◽  
Zhijian Song
Keyword(s):  
Neural Network ◽  
Surgical Navigation ◽  
Conditional Random Field ◽  
Automatic Segmentation ◽  
Convolution Neural Network ◽  
Radiotherapy Planning ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Processing Step

The automatic segmentation of brain tumors in magnetic resonance (MR) images is very important in the diagnosis, radiotherapy planning, surgical navigation and several other clinical processes. As the location, size, shape, boundary of gliomas are heterogeneous, segmenting gliomas and intratumoral structures is very difficult. Besides, the multi-center issue makes it more challenging that multimodal brain gliomas images (such as T1, T2, fluid-attenuated inversion recovery (FLAIR), and T1c images) are from different radiation centers. This paper presents a multimodal, multi-scale, double-pathway, 3D residual convolution neural network (CNN) for automatic gliomas segmentation. In the pre-processing step, a robust gray-level normalization method is proposed to solve the multi-center problem, that the intensity range from deferent centers varies a lot. Then, a doublepathway 3D architecture based on DeepMedic toolkit is trained using multi-modality information to fuse the local and context features. In the post-processing step, a fully connected conditional random field (CRF) is built to improve the performance, filling and connecting the isolated segmentations and holes. Experiments on the Multimodal Brain Tumor Segmentation (BRATS) 2017 and 2019 dataset showed that this methods can delineate the whole tumor with a Dice coefficient, a sensitivity and a positive predictive value (PPV) of 0.88, 0.89 and 0.88, respectively. As for the segmentation of the tumor core and the enhancing area, the sensitivity reached 0.80. The results indicated that this method can segment gliomas and intratumoral structures from multimodal MR images accurately, and it possesses a clinical practice value.

Segmentation of Brain Tumor Tissues in HGG and LGG MR Images Using 3D U-net Convolutional Neural Network

2018 ◽  
Vol 7 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Poornachandra Sandur ◽  
C. Naveena ◽  
V.N. Manjunath Aradhya ◽  
Nagasundara K. B.
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Convolutional Neural Network ◽  
Radiotherapy Planning ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Diagnosis And Prognosis ◽  
Tumor Tissues ◽  
Magnetic Resonance Imaging Mri

The quantitative assessment of tumor extent is necessary for surgical planning, as well as monitoring of tumor growth or shrinkage, and radiotherapy planning. For brain tumors, magnetic resonance imaging (MRI) is used as a standard for diagnosis and prognosis. Manually segmenting brain tumors from 3D MRI volumes is tedious and depends on inter and intra observer variability. In the clinical facilities, a reliable fully automatic brain tumor segmentation method is necessary for the accurate delineation of tumor sub regions. This article presents a 3D U-net Convolutional Neural Network for segmentation of a brain tumor. The proposed method achieves a mean dice score of 0.83, a specificity of 0.80 and a sensitivity of 0.81 for segmenting the whole tumor, and for the tumor core region a mean dice score of 0.76, a specificity of 0.79 and a sensitivity of 0.73. For the enhancing region, the mean dice score is 0.68, a specificity of 0.73 and a sensitivity of 0.77. From the experimental analysis, the proposed U-net model achieved considerably good results compared to the other segmentation models.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

scholarly journals MRI-based brain tumor segmentation using FPGA-accelerated neural network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Siyu Xiong ◽  
Guoqing Wu ◽  
Xitian Fan ◽  
Xuan Feng ◽  
Zhongcheng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Cad Systems ◽  
Segmentation Accuracy ◽  
The Brain

Abstract Background Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors. Results Compared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively. Conclusion We quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors.

scholarly journals Transfer Learning for Classification of 2D Brain MRI Images and Tumor Segmentation

2020 ◽  
Vol 8 (6) ◽  
pp. 2016-2019
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Medical Image ◽  
Brain Mri ◽  
Acquisition Time ◽  
Tumor Segmentation ◽  
Exact Location ◽  
Arduous Task

The focus of the paper is to classify the images into tumorous and non-tumorous and then locate the tumor. Amongst many medical imaging applications segmentation of Brain Tumors is an important and arduous task as the data acquired is disrupted due to artifacts being produced and acquisition time being very less, so classifying and finding the exact location of tumor is one of the most important jobs. In the paper, deep learning specifically the convolutional neural network is used to demonstrate its potential for image classification task. As the learning from available dataset will be low, so we use transfer learning [4] approach, as it is a developing AI strategy that overwhelms with the best outcomes on several image classification assignments because the pre-trained models have gained good knowledge about the features by training on a large number of images. Since, medical image datasets are hard to collect so transfer learning (Alexnet) [1] is used. Later on, after successful classification the aim is to find the exact location of the tumor and this is achieved using basics of image processing inspired by well-known technique of Mask R-CNN [9].

scholarly journals Brain MRI Images Segmentation Based on U-Net Architecture

2021 ◽  
Vol 18 (1) ◽  
pp. 21-27
Author(s):  
Assalah Atiyah ◽  
Khawla Ali
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Performance Metrics ◽  
Brain Mri ◽  
Regular Function ◽  
Survival Rates ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Edge Based ◽  
The Brain

Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented based on region-based and edge-based. Brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study. A comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed. The edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0. 008768, IoU score of 0. 7542, f1 score of 0. 9870, the accuracy of 0. 9935, the precision of 0. 9852, recall of 0. 9888, and specificity of 0. 9951.

scholarly journals MRI Modality-based Brain Tumor Segmentation Using Deep Neural Networks

2021 ◽  
Author(s):  
Pankaj Eknath Kasar ◽  
Shivajirao M. Jadhav ◽  
Vineet Kansal
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Clinical Practice ◽  
Brain Tumors ◽  
Deep Neural Networks ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation

Abstract The tumor detection is major challenging task in brain tumor quantitative evaluation. In recent years, owing to non-invasive and strong soft tissue comparison, Magnetic Resonance Imaging (MRI) has gained great interest. MRI is a commonly used image modality technique to locate brain tumors. An immense amount of data is produced by the MRI. Heterogeneity, isointense and hypointense tumor properties restrict manual segmentation in a fair period of time, thus restricting the use of reliable quantitative measures in clinical practice. In the clinical practice manual segmentation task is quite time consuming and their performance is highly depended on the operator’s experience. Accurate and automated tumor segmentation techniques are also needed; however, the severe spatial and structural heterogeneity of brain tumors makes automatic segmentation a difficult job. This paper proposes fully automatic segmentation of brain tumors using encoder-decoder based convolutional neural networks. The paper focuses on well-known semantic segmentation deep neural networks i.e., UNET and SEGNET for segmenting tumors from Brain MRI images. The networks are trained and tested using freely accessible standard dataset, with Dice Similarity Coefficient (DSC) as metric for whole predicted image i.e., including tumor and background. UNET’s average DSC on test dataset is 0.76 whereas for SEGNET we got average DSC 0.67. The evaluation of results proves that UNET is having better performance than SEGNET.

Integration of optimized neural network and convolutional neural network for automated brain tumor detection

Sensor Review ◽  
2021 ◽  
Vol 41 (1) ◽  
pp. 16-34
Author(s):  
Sathies Kumar Thangarajan ◽  
Arun Chokkalingam
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Detection ◽  
Tumor Segmentation ◽  
Content Type ◽  
Detection Model ◽  
Image Scaling ◽  
Statistical Measures ◽  
Skull Stripping

Purpose The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases. Design/methodology/approach The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model. Findings From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images. Originality/value This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

scholarly journals Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ahmed Abdel Khalek Abdel Razek ◽  
Ahmed Alksas ◽  
Mohamed Shehata ◽  
Amr AbdelKhalek ◽  
Khaled Abdel Baky ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Brain Tumors ◽  
High Performance ◽  
Clinical Applications ◽  
Brain Lesions ◽  
Radiotherapy Planning ◽  
Tumor Segmentation ◽  
Pediatric Tumors ◽  
Neoplastic Lesions

AbstractThis article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient’s prognoses.

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