Brain Tumor Segmentation Method Research Articles

Automated Segmentation of Brain Tumors in MRI: A Comprehensive Review of Deep Learning Approaches

The segmentation of tumor region from brain MRI images is challenging task. Automated approaches are necessary because manual segmentation takes a lot of effort and susceptible to inter-observer variability. This research provides an extensive analysis of deep learning methods for brain tumor segmentation from MRI data. The study begins with a summary of brain tumors and conventional segmentation techniques before doing a thorough evaluation of current developments in automated segmentation methods. We cover many deep learning architectures and their derivatives, specifically designed for brain tumor segmentation. The review also includes methods for differentiating between healthy brain tissue and aberrant tumor tissue. This paper critically examines the benefits, drawbacks, and potential applications of deep learning-based algorithms. It offers insights into current techniques and suggests future prospects for this important field of study.

Improved MRI Brain Tumor Segmentation Method Based on UNet++

Brain tumors are one of the most common tumors with high mortality. Accurate detection and quantitative evaluation of brain tumors are necessary for patient treatment. A reliable and effective method for automatic segmentation of brain tumors by magnetic resonance imaging (MRI) plays a crucial role in patient treatment. In recent years, the Unet++ network structure has shown better performance in image segmentation. However, it is deficient in small-scale feature extraction and segmentation accuracy. In this study, the AC-ResUnet++ network was proposed to address the problems of Unet++ network mentioned above, which combined residual module, convolutional block attention module (CBAM) and Atrous spatial pyramidal pooling (ASPP). The residual module can solve the problem of gradient explosion in the deep network, CBAM can focus on local feature information and enhance the feature learning ability of the network for the target range, and ASPP module can generate different receptive fields and obtain multi-scale contextual feature information. The BraTS 2018 and BraTS 2019 datasets were used to test the proposed network. The Dice similarity coefficient of AC-ResUnet++ in whole tumor, tumor core and enhanced tumor are 0.8521, 0.8696 and 0.7868, respectively. Compared with Unet++ network, the Dice similarity coefficient increased by 1.22%, 5.08% and 2.03% on the baselines. The comprehensive results demonstrate that AC-ResUnet++ performs more effectively in improving the segmentation accuracy of brain tumor.

Proportion Estimation and Multi-Class Classification of Abnormal Brain Cells

Vagueness in the determination of the tumor size creates significant hindrances in planning and quantitatively assessing brain tumor treatments. Non-invasive magnetic resonance imaging has become a primary non-ionizing radiation diagnostic tool for brain cancers. It takes a long time to manually segment the extent of a brain tumor from 3D M.R.I. volumes, and the performance heavily depends on the operator's skill. A precise and automated brain tumor segmentation tool is needed desperately. In this case, an accurate assessment of the tumor's extent requires a reliable automated segmentation method for the brain tumor. The Multimodal Brain Tumor Image Segmentation (BRATS 2020) dataset is used in this paper to demonstrate an automated deep convolutional network, or U-Net, method for brain tumor segmentation. Deep Learning and Transfer Learning are utilized to improve the accuracy and effectiveness in detecting and recognizing different types of brain cancers. The unobserved images' F1 scores were 98% and 99%, respectively.

Sliding transformer with uncertainty estimation for vestibular schwannoma automatic segmentation.

Objective.Automated segmentation of vestibular schwannoma (VS) using magnetic resonance imaging (MRI) can enhance clinical efficiency. Though many advanced methods exist for automated VS segmentation, the accuracy is hindered by ambivalent tumor borders and cystic regions in some patients. In addition, these methods provide results that do not indicate segmentation uncertainty, making their translation into clinical workflows difficult due to potential errors. Providing a definitive segmentation result along with segmentation uncertainty or self-confidence is crucial for the conversion of automated segmentation programs to clinical aid diagnostic tools.Approach.To address these issues, we propose a U-shaped cascade transformer structure with a sliding window that utilizes multiple sliding samples, a segmentation head, and an uncertainty head to obtain both the segmentation mask and uncertainty map. We collected multimodal MRI data from 60 clinical patients with VS from Xuanwu Hospital. Each patient case includes T1-weighted images, contrast-enhanced T1-weighted images, T2-weighted images, and a tumor mask. The images exhibit an in-plane resolution ranging from 0.70 × 0.70 to 0.76 × 0.76 mm, an in-plane matrix spanning from 216 × 256 to 284 × 256, a slice thickness varying between 0.50 and 0.80 mm, and a range of slice numbers from 72 to 120.Main results.Extensive experimental results show that our method achieves comparable or higher results than previous state-of-the-art brain tumor segmentation methods. On our collected multimodal MRI dataset of clinical VS, our method achieved the dice similarity coefficient (DSC) of 96.08% ± 1.30. On a publicly available VS dataset, our method achieved the mean DSC of 94.23% ± 2.53.Significance.The method efficiently solves the VS segmentation task while providing an uncertainty map of the segmentation results, which helps clinical experts review the segmentation results more efficiently and helps to transform the automated segmentation program into a clinical aid diagnostic tool.

Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation.

Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually. This study proposes a new method called the enhanced regularized ensemble encoder-decoder network (EREEDN) for more accurate brain tumor segmentation. The EREEDN model first preprocesses the MRI data by normalizing the intensity levels. It then uses a series of autoencoder networks to segment the tumor. These autoencoder networks are trained using back-propagation and gradient descent. To prevent overfitting, the EREEDN model also uses L2 regularization and dropout mechanisms. The EREEDN model was evaluated on the BraTS 2020 dataset. It achieved high performance on various metrics, including accuracy, sensitivity, specificity, and dice coefficient score. The EREEDN model outperformed other methods on the BraTS 2020 dataset. The EREEDN model is a promising new method for brain tumor segmentation. It is more accurate and efficient than previous methods. Future studies will focus on improving the performance of the EREEDN model on complex tumors.

EDLNet: ensemble deep learning network model for automatic brain tumor classification and segmentation

The brain’s abnormal and uncontrollable cell partitioning is a severe cancer disease. The tissues around the brain or the skull induce this tumor to develop spontaneously. For the treatment of a brain tumor, surgical techniques are typically preferred. Deep learning models in the biomedical field have recently attracted a lot of attention for detecting and treating diseases. This article proposes a new Ensemble Deep Learning Network (EDLNet) model. This research uses the Modified Faster RCNN approach to classify brain MRI scan images into cancerous and non-cancerous. A deep recurrent convolutional neural network (DRCNN)-based diagnostic method for early-stage brain tumor segmentation is presented. The evaluation outcomes show that the proposed tumor classification and segmentation model’s performance accurately segments tissues from MRI images. For the analysis of the proposed model, two different publicly available datasets (D1&D2) are used. For D1 and D2 datasets, a total of 99.76% and 99.87% accuracies are achieved by the proposed model. The performance results of the proposed model are more effective than the state-of-the-art network models as per the experimental results. Communicated by Ramaswamy H. Sarma

Multimodal Transformer of Incomplete MRI Data for Brain Tumor Segmentation.

Accurate segmentation of brain tumors plays an important role for clinical diagnosis and treatment. Multimodal magnetic resonance imaging (MRI) can provide rich and complementary information for accurate brain tumor segmentation. However, some modalities may be absent in clinical practice. It is still challenging to integrate the incomplete multimodal MRI data for accurate segmentation of brain tumors. In this paper, we propose a brain tumor segmentation method based on multimodal transformer network with incomplete multimodal MRI data. The network is based on U-Net architecture consisting of modality specific encoders, multimodal transformer and multimodal shared-weight decoder. First, a convolutional encoder is built to extract the specific features of each modality. Then, a multimodal transformer is proposed to model the correlations of multimodal features and learn the features of missing modalities. Finally, a multimodal shared-weight decoder is proposed to progressively aggregate the multimodal and multi-level features with spatial and channel self-attention modules for brain tumor segmentation. A missing-full complementary learning strategy is used to explore the latent correlation between the missing and full modalities for feature compensation. For evaluation, our method is tested on the multimodal MRI data from BraTS 2018, BraTS 2019 and BraTS 2020 datasets. The extensive results demonstrate that our method outperforms the state-of-the-art methods for brain tumor segmentation on most subsets of missing modalities.

WS-MTST: Weakly Supervised Multi-Label Brain Tumor Segmentation With Transformers.

Brain tumor segmentation is a key step in brain cancer diagnosis. Segmentation of brain tumor sub-regions, including necrotic, enhancing, and edematous regions, can provide more detailed guidance for clinical diagnosis. Weakly supervised brain tumor segmentation methods have received much attention because they do not require time-consuming pixel-level annotations. However, existing weakly supervised methods focus on the segmentation of the entire tumor region while ignoring the challenging task of multi-label segmentation for the tumor sub-regions. In this article, we propose a weakly supervised approach to solve the multi-label brain tumor segmentation problem. To the best of our knowledge, it's the first end-to-end multi-label weakly supervised segmentation model applied to brain tumor segmentation. With well-designed loss functions and a contrastive learning pre-training process, our proposed Transformer-based segmentation method (WS-MTST) has the ability to perform segmentation of brain tumor sub-regions. We conduct comprehensive experiments and demonstrate that our method reaches the state-of-the-art on the popular brain tumor dataset BraTS (from 2018 to 2020).

Robust U-Net-Based Approach for Accurate Brain Tumor Segmentation Using Multimodal MRI Data

Detecting and quantifying the extent of brain tumors poses a formidable challenge in medical centers. Magnetic Resonance Imaging (MRI) has developed as a non-invasive brain cancers' primary diagnostic tool, offering the crucial advantage of avoiding ionizing radiation. Brain tumor manually segmented boundaries within 3D MRI volumes is an exceedingly time-intensive task, heavily reliant on operator expertise. Among brain tumors, gliomas stand out as the prevalent and highly malignant, significantly impacting patients' life expectancy, particularly at their highest grade. Recognizing the pressing need for a reliable, completely automatic segmentation technique to efficiently assess tumor extent, this study introduces a robust approach. A completely automated brain tumor segmentation method is proposed, leveraging U-Net-based deep convolutional networks. This approach underwent rigorous evaluation on the Multimodal Brain Tumor Image Segmentation BraTS-19 dataset a widely recognized medical image analysis dataset featuring multimodal MRI scans of brain tumors, including glioblastoma, anaplastic astrocytoma, and lower-grade glioma, coupled with corresponding manual tumor segmentations. This dataset serves as a pivotal resource for advancing automatic brain tumor segmentation techniques and assessing their performance using metrics like the Dice score, which achieved 92% for entire tumor. Cross-validation results affirm the efficiency and promise of our method in achieving accurate segmentation.

A NOVEL DEEP LEARNING METHOD FOR BRAIN TUMOR SEGMENTATION IN MAGNETIC RESONANCE IMAGES BASED ON RESIDUAL UNITS AND MODIFIED U-NET MODEL

Brain tumors are among the most deadly forms of cancer, as the brain is a crucial organ for human activity. Early detection and treatment are key to recovery. An expert’s final decision on tumor diagnosis mainly depends on the evaluation of Magnetic Resonance Imaging (MRI) images. However, the traditional manual assessment process is time-consuming, error-prone, and relies on the experience and knowledge of doctors, along with other unstable factors. An automated brain tumor detection system can assist radiologists and internal medicine experts in detecting and diagnosing brain tumors. This study proposes a novel deep learning model that combines residual units with a modified U-Net framework for brain tumor segmentation tasks in brain MR images. In this study, the U-Net-based framework is implemented with a stack of neural units and residual units and uses Leaky Rectified Linear Unit (LReLU) as the model’s activation function. First, neural units are added before the first layer of downsampling and upsampling to enhance feature propagation and reuse. Then, the stacking of residual blocks is applied to achieve deep semantic information extraction for downsampling and pixel classification for upsampling. Finally, a single-layer convolution outputs the predicted segmented images. The experimental results show that the segmentation Dice Similarity Coefficient of this model is 90.79%, and the model demonstrates better segmentation accuracy than other research models.

DE-UFormer: U-shaped dual encoder architectures for brain tumor segmentation.

Objective. In brain tumor segmentation tasks, the convolutional neural network (CNN) or transformer is usually acted as the encoder since the encoder is necessary to be used. On one hand, the convolution operation of CNN has advantages of extracting local information although its performance of obtaining global expressions is bad. On the other hand, the attention mechanism of the transformer is good at establishing remote dependencies while it is lacking in the ability to extract high-precision local information. Either high precision local information or global contextual information is crucial in brain tumor segmentation tasks. The aim of this paper is to propose a brain tumor segmentation model that can simultaneously extract and fuse high-precision local and global contextual information.Approach. We propose a network model DE-Uformer with dual encoders to obtain local features and global representations using both CNN encoder and Transformer encoder. On the basis of this, we further propose the nested encoder-aware feature fusion (NEaFF) module for effective deep fusion of the information under each dimension. It may establishe remote dependencies of features under a single encoder via the spatial attention Transformer. Meanwhile ,it also investigates how features extracted from two encoders are related with the cross-encoder attention transformer.Main results. The proposed algorithm segmentation have been performed on BraTS2020 dataset and private meningioma dataset. Results show that it is significantly better than current state-of-the-art brain tumor segmentation methods.Significance. The method proposed in this paper greatly improves the accuracy of brain tumor segmentation. This advancement helps healthcare professionals perform a more comprehensive analysis and assessment of brain tumors, thereby improving diagnostic accuracy and reliability. This fully automated brain model segmentation model with high accuracy is of great significance for critical decisions made by physicians in selecting treatment strategies and preoperative planning.

An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering.

Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.

Uncertainty-guided transformer for brain tumor segmentation.

Multi-model data can enhance brain tumor segmentation for the rich information it provides. However, it also introduces some redundant information that interferes with the segmentation estimation, as some modalities may catch features irrelevant to the tissue of interest. Besides, the ambiguous boundaries and irregulate shapes of different grade tumors lead to a non-confidence estimate of segmentation quality. Given these concerns, we exploit an uncertainty-guided U-shaped transformer with multiple heads to construct drop-out format masks for robust training. Specifically, our drop-out masks are composed of boundary mask, prior probability mask, and conditional probability mask, which can help our approach focus more on uncertainty regions. Extensive experimental results show that our method achieves comparable or higher results than previous state-of-the-art brain tumor segmentation methods, achieving average dice coefficients of [Formula: see text] and Hausdorff distance of 4.91 on the BraTS2021 dataset. Our code is freely available at https://github.com/chaineypung/BTS-UGT.

Review of deep learning-driven MRI brain tumor detection and segmentation methods

The application of deep learning in the field of medical imaging has become increasingly widespread, greatly promoting the advancement and development of Magnetic Resonance Imaging (MRI) brain tumor detection and segmentation techniques. Therefore, a comprehensive review of deep learning-based methods for MRI brain tumor detection and segmentation was conducted. This review introduces the basic concepts of brain tumors and MRI brain tumor detection and segmentation, discusses the specific applications and typical methods of deep learning in MRI brain tumor detection and segmentation, and analyzes and compares the performance and advantages and disadvantages of different methods. Additionally, representative brain tu-mor segmentation dataset (BraTS) and its evaluation metrics are introduced, upon which the performance of various deep learning-based brain tumor segmentation methods on the BraTS 2019-2022 dataset is compared. Lastly, the challenges and future development trends in deep learning-based MRI brain tumor detection and segmentation methods are summarized and anticipated.

Respiratory Exertion Using Virtual Reality

Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess these tumors. The large amount of data produced by MRI prevents manual segmentation in a reasonable time. Automatic and reliable segmentation methods are required. Independent projection-based classification (LIPC) is used to segment the tumor region. Here, also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Here, proposed an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3x3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against over fitting, given the fewer number of weights in the network. Results reported on public benchmarks reveal that our architecture achieves competitive accuracy compared to the state- of-the-art brain tumor segmentation methods while being computationally efficient.. Index Terms—Digital Image Processing,Positron Emission Tomography,PhotomultiplierTube,ComputedTomography,Magne tic Resonance Image,Fluid-Attenuated Inversion Recovery ,Single Photon Emission Computed Tomography TR Repetition Time, echo time ,Region of Interest.

Overview of convolutional neural networks architectures for brain tumor segmentation

<p><span lang="EN-US">Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset.</span></p>

Comparison of Brain Tumor Segmentation Methods Based on Different Algorithms Using MRI Images

Brain tumor is a serious disease for human beings. MRI is the most widely used method for its innocuousness since people do not need to be exposure to radioactivity. The segmentation on MRI images is a vital step in tumor detection. To improve efficiency and accuracy of the segmentation, scientists apply different algorithms in this process. This paper focuses on three particular algorithms including Connected component label algorithm (CCLA), Watershed algorithm (WSA) and Fuzzy C-means clustering algorithm (FCCA). The principles and applied procedures of these three algorithms are introduced. Basing on this background information, algorithms are compared from three aspects. Fuzzy C-means clustering algorithm is considered as more efficient and accurate among these three algorithms. All algorithms have good research prospects, and the segmentation result can be improved through the improvement on algorithms.

DenseUNet+: A novel hybrid segmentation approach based on multi-modality images for brain tumor segmentation

Segmentation of brain tumors is of great importance for patients in clinical diagnosis and treatment. For this reason, experts try to identify border regions of special importance using multimodal images from magnetic resonance imaging systems. In some images, border regions may be intertwined. As a result, this situation leads experts to make incomplete or wrong decisions. This paper presents DenseUNet+, a new deep learning-based approach to perform segmentation with high accuracy using multimodal images. In the DenseUNet+ model, data from four different modalities were used together in dense block structures. Afterward, linear operations were applied to these data and then the concatenate operation was performed. The results obtained in this way were transferred to the decoder layer. The proposed method was also compared with state-of-the-art (SOTA) studies using the same dataset by using dice and jaccard metrics in the BraTS2021 and FeTS2021 datasets. As a result of the comparison, dice and jaccard evaluation metrics for the BraTS2021 dataset were 95% and 88%, respectively, and 86% and 87% performance values were obtained for FeTS2021, respectively. It has been determined that the performance results are better than many SOTA brain tumor segmentation methods.

Joint learning-based feature reconstruction and enhanced network for incomplete multi-modal brain tumor segmentation

Multimodal Magnetic Resonance Imaging (MRI) can provide valuable complementary information and substantially enhance the performance of brain tumor segmentation. However, it is common for certain modalities to be absent or missing during clinical diagnosis, which can significantly impair segmentation techniques that rely on complete modalities. Current advanced methods attempt to address this challenge by developing shared feature representations via modal fusion to handle different missing modality situations. Considering the importance of missing modality information in multimodal segmentation, this paper utilize a feature reconstruction method to recover the missing information, and proposes a joint learning-based feature reconstruction and enhancement method for incomplete modality brain tumor segmentation. The method leverages an information learning mechanism to transfer information from the complete modality to a single modality, enabling it to obtain complete brain tumor information, even without the support of other modalities. Additionally, the method incorporates a module for reconstructing missing modality features, which recovers fused features of the absent modality through utilizing the abundant potential information obtained from the available modalities. Furthermore, the feature enhancement mechanism improves shared feature representation by utilizing the information obtained from the missing modalities that have been reconstructed. These processes enable the method to obtain more comprehensive information regarding brain tumors in various missing modality circumstances, thereby enhancing the model’s robustness. The performance of the proposed model was evaluated on BraTS datasets and compared with other deep learning algorithms using Dice similarity scores. On the BraTS2018 dataset, the proposed algorithm achieved a Dice similarity score of 86.28%, 77.02%, and 59.64% for whole tumors, tumor cores, and enhanced tumors, respectively. These results demonstrate the superiority of our framework over state-of-the-art methods in missing modalities situations.

Gradient-assisted deep model for brain tumor segmentation by multi-modality MRI volumes

In clinical diagnosis, doctors make treatment strategies through key factors such as the location, shape and size of tumors. With the development of computer technology, the advantages of deep learning in medical image segmentation are increasingly prominent. Gliomas have the characteristics of diffuse infiltration, blurred boundary and swelling of brain tissue in affected areas, which makes it challenging to accurately segment brain tumors near the intersection area. The existing algorithms have achieved great results in segmenting tumors in terms of gray information. However they ignored the gradient of tumor boundary areas. The complexity of multi-modality MRI and the vast differences between brain tumor areas make it difficult to segment brain tumors efficiently and accurately. To solve the above problems, we propose a gradient-assisted multi-category brain tumor segmentation method(GAM-Net). GAM-Net includes three branches: (1) double convolutional encoder, which could capture rich features from multi-modality MRI; (2) gradient extraction branch, which could generate gradient features to assist area segmentation; and (3) gradient-driven decoder which could provide fusion contour information and encoding features effectively. We evaluated the effectiveness of the proposed algorithm on BraTS2020 dataset, of which 295 cases are used as training sets and 74 cases as test sets. Finally, the Dice Similarity Coefficients (DSC) of the proposed algorithm in whole tumor (WT), tumor core (TC), and enhanced tumor (ET) are 0.8991, 0.8402, and 0.7580 respectively. Average DSC reaches 0.8324. Experimental results show that GAM-Net can be successfully applied to segment brain tumors and thus helpful in diagnosis and treatment.