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Salp Swarm Algorithm with Multilevel Thresholding Based brain tumor segmentation Model

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Computers, Materials & Continua 2023年第3期74卷 6775-6788页

作者： Hanan T.Halawani Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran55461Saudi Arabia

Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma for clinical examination. Biomedical image segmentation plays avital role in healthcare decision making process which also helps to identifythe affected regions in the MRI. Though numerous segmentation models areavailable in the literature, it is still needed to develop effective segmentationmodels for BT. This study develops a salp swarm algorithm with multi-levelthresholding based brain tumor segmentation (SSAMLT-BTS) model. Thepresented SSAMLT-BTS model initially employs bilateral filtering based onnoise removal and skull stripping as a pre-processing phase. In addition,Otsu thresholding approach is applied to segment the biomedical imagesand the optimum threshold values are chosen by the use of SSA. Finally,active contour (AC) technique is used to identify the suspicious regions in themedical image. A comprehensive experimental analysis of the SSAMLT-BTSmodel is performed using benchmark dataset and the outcomes are inspectedin many aspects. The simulation outcomes reported the improved outcomesof the SSAMLT-BTS model over recent approaches with maximum accuracyof 95.95%.

关键词： brain tumor segmentation noise removal multilevel thresholding healthcare pre-processing

3D Kronecker Convolutional Feature Pyramid for brain tumor Semantic segmentation in MR Imaging

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Computers, Materials & Continua 2023年第9期76卷 2861-2877页

作者： Kainat Nazir Tahir Mustafa Madni Uzair Iqbal Janjua Umer Javed Muhammad Attique Khan Usman Tariq Jae-Hyuk Cha Medical Imaging and Diagnostics Lab NCAIDepartment of Computer ScienceCOMSATS University IslamabadIslamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusWahPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Department of Management Information Systems College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones.Diagnosing a brain tumor usually begins with magnetic resonance imaging(MRI).The manual brain tumor diagnosis from the MRO images always requires an expert radiologist.However,this process is time-consuming and costly.Therefore,a computerized technique is required for brain tumor detection in MRI images.Using the MRI,a novel mechanism of the three-dimensional(3D)Kronecker convolution feature pyramid(KCFP)is used to segment brain tumors,resolving the pixel loss and weak processing of multi-scale lesions.A single dilation rate was replaced with the 3D Kronecker convolution,while local feature learning was performed using the 3D Feature Selection(3DFSC).A 3D KCFP was added at the end of 3DFSC to resolve weak processing of multi-scale lesions,yielding efficient segmentation of brain tumors of different sizes.A 3D connected component analysis with a global threshold was used as a post-processing technique.The standard Multimodal brain tumor segmentation 2020 dataset was used for model validation.Our 3D KCFP model performed exceptionally well compared to other benchmark schemes with a dice similarity coefficient of 0.90,0.80,and 0.84 for the whole tumor,enhancing tumor,and tumor core,respectively.Overall,the proposed model was efficient in brain tumor segmentation,which may facilitate medical practitioners for an appropriate diagnosis for future treatment planning.

关键词： brain tumor segmentation connect component analysis deep learning kronecker convolution magnetic resonance imaging

Clinical Knowledge-Based Hybrid Swin Transformer for brain tumor segmentation

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Computers, Materials & Continua 2023年第9期76卷 3797-3811页

作者： Xiaoliang Lei Xiaosheng Yu Hao Wu Chengdong Wu Jingsi Zhang College of Information Science and Engineering Northeastern UniversityShenyang110819China Faculty of Robot Science and Engineer Northeastern UniversityShenyang110819China Faculty of Engineering Macquarie UniversitySydneyNSW2109Australia

Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging(MRI)imaging is crucial in the pre-surgical planning of brain tumor malignancy.MRI images’heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging.Furthermore,recent studies have yet to fully employ MRI sequences’considerable and supplementary information,which offers critical a priori knowledge.This paper proposes a clinical knowledge-based hybrid Swin Transformermultimodal brain tumor segmentation algorithmbased on how experts identify malignancies from MRI images.During the encoder phase,a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a Convolutional Neural Network(CNN)-based backbone to represent local features have been constructed.Instead of directly connecting all the MRI sequences,the proposed method re-organizes them and splits them into two groups based on MRI principles and characteristics:T1 and T1ce,T2 and Flair.These aggregated images are received by the dual-stem Swin Transformer-based encoder branch,and the multimodal sequence-interacted cross-attention module(MScAM)captures the interactive information between two sets of linked modalities in each stage.In the CNN-based encoder branch,a triple down-sampling module(TDsM)has been proposed to balance the performance while downsampling.In the final stage of the encoder,the feature maps acquired from two branches are concatenated as input to the decoder,which is constrained by MScAM outputs.The proposed method has been evaluated on datasets from the MICCAI BraTS2021 Challenge.The results of the experiments demonstrate that the method algorithm can precisely segment brain tumors,especially the portions within tumors.

关键词： brain tumor segmentation swin transformer multimodal clinical knowledge

brain tumor segmentation of HGG and LGG MRI Images Using WFL-Based 3D U-Net

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Journal of Biomedical Science and Engineering 2022年第10期15卷 241-260页

作者： Ahliddin Shomirov Jing Zhang Mohammad Masum Billah School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan China

The semantic segmentation of a brain tumor is the essential stage in medical treatment planning. Due to the different characteristics of tumors, one of the main difficulties in image segmentation is the severe imbalance between classes. Also, a dataset with imbalanced classes is a common problem in multimodal 3D brain MRIs. Despite these problems, most studies in brain tumor segmentation are biased toward the overrepresented tumor class (majority class) and ignore the small size tumor class (minority class). In this paper, we propose an improved loss function Weighted Focal Loss (WFL), based on 3D U-Net to enhance the prediction of brain tumor segmentation. Using our proposed loss function (WFL) solves the imbalance between classes and the imbalance between weights by giving higher weights to the minority and lower weights to the majority. After assigning these weights to different pixel values, our work is able to resolve pixel degradation, which is one of the limitations of the loss function during model training. Based on our experiments, the proposed function (WFL) on the 3D U-Net model for high-grade glioma (HGG) and low-grade glioma (LGG) in the brain tumor segmentation Challenge (BraTS) 2019 dataset has shown promising results for tumor core (TC), whole tumor (WT) and enhanced tumor (ET) with average dice scores of HGG: 0.830, 0.913, 0.815 and Dice scores of LGG for TC: 0.731, WT: 0.775 and ET: 0.685. Moreover, we deployed our training on BraTS 2020 in which we obtained mean Dice scores of HGG: TC: 0.843, WT: 0.892, ET: 0.871 and Dice scores of LGG: 0.7501, 0.7985, 0.6103 for TC, WT and ET, respectively.

关键词： brain tumor segmentation 3D U-Net WFL HGG and LGG

Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

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Frontiers of Information Technology & Electronic Engineering 2018年第4期19卷 471-480页

作者： Ji-jun TONG Peng ZHANG Yu-xiang WENG Dan-hua ZHU School of lnformation Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China Department of Neurosurgery The First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003China State Key Laboratory for Diagnosis and Treatment of lnfectious Diseases Collaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesThe First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003China

The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully automatic brain tumor segmentation method based on kernel sparse coding. It is validated with 3D multiple-modality magnetic resonance imaging(MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learning is used to extract the nonlinear features to construct five adaptive dictionaries for healthy tissues, necrosis, edema, non-enhancing tumor, and enhancing tumor tissues. Sparse coding is performed on the feature vectors extracted from the original MRI images, which are a patch of m×m×m around the voxel. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online evaluation system where the evaluation metrics dice score, positive predictive value(PPV), sensitivity, and kappa are used. The results demonstrate that the proposed method has good performance on the complete tumor region(dice: 0.83; PPV: 0.84; sensitivity: 0.82), while slightly worse performance on the tumor core(dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor(dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore, it is a potential method in differentiation of healthy and pathological tissues.

关键词： brain tumor segmentation Kernel method Sparse coding Dictionary learning

Double attention U-Net for brain tumor MR image segmentation

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International Journal of Intelligent Computing and Cybernetics 2021年第3期14卷 467-479页

作者： Na Li Kai Ren College of Computer Sciences South-Central University for NationalitiesWuhanChina

Purpose-Automatic 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/approach-In 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.Findings-To verify the effectiveness of the DAU-Net,experiments were carried out on the BRATS2018 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/value-The experimental results show that the proposed method has good performance in the segmentation of brain tumors.The proposed method has potential clinical applicability.

关键词： brain tumor segmentation U-Net model Double attention mechanism

Automated brain tumor segmentation from multimodal MRI data based on Tamura texture feature and an ensemble SVM classifier

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International Journal of Intelligent Computing and Cybernetics 2019年第4期12卷 466-480页

作者： Li Na Xiong Zhiyong Deng Tianqi Ren Kai Department of Computer Science South-Central University for NationalitiesWuhanChina

Purpose–Theprecisesegmentation ofbraintumors isthe mostimportantandcrucialstepintheir diagnosis and treatment.Due to the presence of noise,uneven gray levels,blurred boundaries and edema around the brain tumor region,the brain tumor image has indistinct features in the tumor region,which pose a problem for diagnostics.The paper aims to discuss these issues.Design/methodology/approach–In this paper,the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine(SVM)structure.In the proposed technique,124 features of each voxel are extracted,including Tamura texture features and grayscale features.Then,these features are ranked using the SVM-Recursive Feature Elimination method,which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs.Finally,the bagging random sampling method is utilized to construct the ensemble SVM classifierbased on a weighted voting mechanism to classify the types of voxel.Findings–The experiments are conducted over a sample data set to be called BraTS2015.The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors,especially the feature of line-likeness.The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods.Originality/value–The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.

关键词： An ensemble SVM brain tumor segmentation MRI Tamura texture feature

MRI brain tumor segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks

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Computer Modeling in Engineering & Sciences 2021年第8期128卷 427-445页

作者： Juhong Tie Hui Peng Jiliu Zhou College of Computer Science Sichuan UniversityChengdu610065China School of Software Engineering Chengdu University of Information TechnologyChengdu610225China School of Computer Science Chengdu University of Information TechnologyChengdu610225China

The main task of magnetic resonance imaging (MRI) automatic brain tumor segmentation is to automaticallysegment the brain tumor edema, peritumoral edema, endoscopic core, enhancing tumor core and nonenhancingtumor core from 3D MR images. Because the location, size, shape and intensity of brain tumors vary greatly, itis very difficult to segment these brain tumor regions automatically. In this paper, by combining the advantagesof DenseNet and ResNet, we proposed a new 3D U-Net with dense encoder blocks and residual decoder blocks.We used dense blocks in the encoder part and residual blocks in the decoder part. The number of output featuremaps increases with the network layers in contracting path of encoder, which is consistent with the characteristicsof dense blocks. Using dense blocks can decrease the number of network parameters, deepen network layers,strengthen feature propagation, alleviate vanishing-gradient and enlarge receptive fields. The residual blockswere used in the decoder to replace the convolution neural block of original U-Net, which made the networkperformance better. Our proposed approach was trained and validated on the BraTS2019 training and validationdata set. We obtained dice scores of 0.901, 0.815 and 0.766 for whole tumor, tumor core and enhancing tumorcore respectively on the BraTS2019 validation data set. Our method has the better performance than the original3D U-Net. The results of our experiment demonstrate that compared with some state-of-the-art methods, ourapproach is a competitive automatic brain tumor segmentation method.

关键词： MRI brain tumor segmentation U-Net dense block residual block

Deep learning network with Euclidean similarity factor for brain MR tumor segmentation and volume estimation

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International Journal of Modeling, Simulation, and Scientific Computing 2019年第6期10卷 57-84页

作者： G.Anand Kumar P.V.Sridevi Department of Electronics and Communication Engineering Gayatri Vidya Parishad College of Engineering(Autonomous)Visakhapatnam Andhra PradeshIndia Department of Electronics and Communication Engineering Andhra University College of Engineering(Autonomous)Visakhapatnam Andhra PradeshIndia

The major goal of this paper is to isolate tumor region from nontumor regions and the estimation of tumor volume.Accurate segmentation is not an easy task due to the varying size,shape and location of the tumor.After segmentation,volume estimation is necessary in order to accurately estimate the tumor volume.By exactly estimating the volume of abnormal tissue,physicians can do excellent prognosis,clinical planning and dosage estimation.This paper describes a new Euclidean Similarity factor(ESF)based active contour model with deep learning for segmenting the tumor region into complete,core and enhanced tumor portions.Initially,the ESF considers the spatial distances and intensity differences of the region automatically to detect the tumor region.It preserves the image details but removes the noisy details.Then,the 3D Convolutional Neural Network(3D CNN)segments the tumor by automatically extracting spatiotemporal features.Finally,the extended shoelace method estimates the volume of the tumor accurately for n-sided polygons.The simulation result achieves a high accuracy of 92%and Jaccard index of 0.912 and computes the tumor volume with effective performance than existing approaches.

关键词： Active contour brain tumor segmentation convolutional neural network Euclidean similarity factor magnetic resonance imaging