Accuracy Of Brain Tumor Segmentation Research Articles

Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors.

Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy. Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data. We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

Fully Automatic Glioma Segmentation Algorithm of Magnetic Resonance Imaging Based on 3D-UNet With More Global Contextual Feature Extraction: An Improvement on Insufficient Extraction of Global Features

The fully automatic segmentation of glioma and its subregions is fundamental for computer-aided clinical diagnosis of tumors. In the segmentation process of brain magnetic resonance imaging (MRI), convolutional neural networks with small convolutional kernels can only capture local features and are ineffective at integrating global features, which narrows the receptive field and leads to insufficient segmentation accuracy. This study aims to use dilated convolution to address the problem of inadequate global feature extraction in 3D-UNet. 1) Algorithm construction: A 3D-UNet model with three pathways for more global contextual feature extraction, or 3DGE-UNet, was proposed in the paper. By using publicly available datasets from the Brain Tumor Segmentation Challenge (BraTS) of 2019 (335 patient cases), a global contextual feature extraction (GE) module was designed. This module was integrated at the first, second, and third skip connections of the 3D UNet network. The module was utilized to fully extract global features at different scales from the images. The global features thus extracted were then overlaid with the upsampled feature maps to expand the model's receptive field and achieve deep fusion of features at different scales, thereby facilitating end-to-end automatic segmentation of brain tumors. 2) Algorithm validation: The image data were sourced from the BraTs 2019 dataset, which included the preoperative MRI images of 335 patients across four modalities (T1, T1ce, T2, and FLAIR) and a tumor image with annotations made by physicians. The dataset was divided into the training, the validation, and the testing sets at an 8∶1∶1 ratio. Physician-labelled tumor images were used as the gold standard. Then, the algorithm's segmentation performance on the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) was evaluated in the test set using the Dice coefficient (for overall effectiveness evaluation), sensitivity (detection rate of lesion areas), and 95% Hausdorff distance (segmentation accuracy of tumor boundaries). The performance was tested using both the 3D-UNet model without the GE module and the 3DGE-UNet model with the GE module to internally validate the effectiveness of the GE module setup. Additionally, the performance indicators were evaluated using the 3DGE-UNet model, ResUNet, UNet++, nnUNet, and UNETR, and the convergence of these five algorithm models was compared to externally validate the effectiveness of the 3DGE-UNet model. 1) In internal validation, the enhanced 3DGE-UNet model achieved Dice mean values of 91.47%, 87.14%, and 83.35% for segmenting the WT, TC, and ET regions in the test set, respectively, producing the optimal values for comprehensive evaluation. These scores were superior to the corresponding scores of the traditional 3D-UNet model, which were 89.79%, 85.13%, and 80.90%, indicating a significant improvement in segmentation accuracy across all three regions (P<0.05). Compared with the 3D-UNet model, the 3DGE-UNet model demonstrated higher sensitivity for ET (86.46% vs. 80.77%) (P<0.05) , demonstrating better performance in the detection of all the lesion areas. When dealing with lesion areas, the 3DGE-UNet model tended to correctly identify and capture the positive areas in a more comprehensive way, thereby effectively reducing the likelihood of missed diagnoses. The 3DGE-UNet model also exhibited exceptional performance in segmenting the edges of WT, producing a mean 95% Hausdorff distance superior to that of the 3D-UNet model (8.17 mm vs. 13.61 mm, P<0.05). However, its performance for TC (8.73 mm vs. 7.47 mm) and ET (6.21 mm vs. 5.45 mm) was similar to that of the 3D-UNet model. 2) In the external validation, the other four algorithms outperformed the 3DGE-UNet model only in the mean Dice for TC (87.25%), the mean sensitivity for WT (94.59%), the mean sensitivity for TC (86.98%), and the mean 95% Hausdorff distance for ET (5.37 mm). Nonetheless, these differences were not statistically significant (P>0.05). The 3DGE-UNet model demonstrated rapid convergence during the training phase, outpacing the other external models. The 3DGE-UNet model can effectively extract and fuse feature information on different scales, improving the accuracy of brain tumor segmentation.

Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures

Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security.

Image-based MRI detection of brain tumours using convolutional neural networks

Rapid and uncontrolled cellular proliferation is what distinguishes a brain tumor. Unfortunately, brain tumors cannot be prevented other than via surgery. As predicted, deep learning may help diagnose and cure brain cancers. The segmentation approach has been widely studied for brain tumor removal. This uses the segmentation approach, one of the most advanced methods for object detection and categorization. To efficiently assess the tumor's size, an accurate and automated brain tumor segmentation approach is needed. We present a fully automated brain tumor separation method for imaging investigations. The approach has been developed with convolutional neural networks. The Multimodal Brain Tumor Image Segmentation (BRATS) datasets tested our strategy. This result suggests that DL should investigate heterogeneous MRI image segmentation to improve brain tumor segmentation accuracy and efficacy. This study may lead to more accurate medical diagnoses and treatments. Researchers in this study also found a way to automatically find cancerous tumours by using the Grey Level Co-Occurrence Matrix (GLCM) and discrete wavelet transform (DWT) to find features in MRI images. They then used a CNN to guess the final prognosis. The preceding section details this technique. When compared to the other algorithm, the CNN method uses computer resources better.

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.

Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy

Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy

DenseTrans: Multimodal Brain Tumor Segmentation Using Swin Transformer

Aiming at the task of automatic brain tumor segmentation, this paper proposes a new DenseTrans network. In order to alleviate the problem that convolutional neural networks(CNN) cannot establish long-distance dependence and obtain global context information, swin transformer is introduced into UNet++ network, and local feature information is extracted by convolutional layer in UNet++. then, in the high resolution layer, shift window operation of swin transformer is utilized and self-attention learning windows are stacked to obtain global feature information and the capability of long-distance dependency modeling. meanwhile, in order to alleviate the secondary increase of computational complexity caused by full self-attention learning in transformer, deep separable convolution and control of swin transformer layers are adopted to achieve a balance between the increase of accuracy of brain tumor segmentation and the increase of computational complexity. on BraTs2021 data validation set, model performance is as follows: the dice dimilarity score was 93.2%,86.2%,88.3% in the whole tumor,tumor core and enhancing tumor, hausdorff distance(95%) values of 4.58mm,14.8mm and 12.2mm, and a lightweight model with 21.3M parameters and 212G flops was obtained by depth-separable convolution and other operations. in conclusion, the proposed model effectively improves the segmentation accuracy of brain tumors and has high clinical value.

Automated Segmentation of Brain Tumor MRI Images Using Deep Learning

Segmenting brain tumors automatically using MR data is crucial for disease investigation and monitoring. Due to the aggressive nature and diversity of gliomas, well-organized and exact segmentation methods are used to classify tumors intra-tumorally. The proposed technique uses a Gray Level Co-occurrence matrix extraction of features approach to strip out unwanted details from the images. In comparison with the current state of the art, the accuracy of brain tumor segmentation was significantly improved using Convolutional Neural Networks, which are frequently used in the field of biomedical image segmentation. By merging the results of two separate segmentation networks, the proposed method demonstrates a major but simple combinatorial strategy that, as a direct consequence, yields much more precise and complete estimates. A U-Net and a Three-Dimensional Convolutional Neural Network. These networks are used to break up images into their component parts. Following that, the prediction was constructed using two distinct models that were combined in a number of ways. In comparison to existing state-of-the-art designs, the proposed method achieves the mean accuracy (%) of 99.40, 98.46, 98.29, precision (%) of 99.41, 98.51, 98.35, F-Score (%) of 99.4, 98.29, 98.46 and sensitivity (%) of 99.39, 98.41, 98.25 for the whole tumor, enhanced tumor, tumor core on the validation set, respectively.

Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

Brain tumors are one of the most common causes of death that have been widely investigated by scholars in research areas, including care and prevention. Despite various empirical studies on the brain tumor segmentatin, there is still a need for further investigation. This fact is more needed in the automatic methods of brain tumors detection. In the present study, a new method for improving brain tumor segmentation accuracy based on super-pixel and fast primal dual (PD) algorithms has been proposed. The proposed method detects brain tumor tissue in Flair-MRI imaging in BRATS2012 dataset. This method detects the primary borders of tumors using a super-pixel algorithm, and improves brain tumor borders using fast PD in Markov random field optimization. Then, post-processing processes are used to delete white brain areas. Finally, an active contour algorithm was employed to display tumor area. Different experiments were carried on the proposed method and qualitative and quantitative criteria such as dice similarity measure, accuracy and F-measure were used for evaluation. The obtained results showed the efficiency of the proposed method, such that in the accuracy and sensitivity of 86.59 and 88.57% and F1-Measure 86.37 were obtained, respectively.

Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy

A brain tumor consists of cells showing abnormal brain growth. The area of the brain tumor significantly affects choosing the type of treatment and following the course of the disease during the treatment. At the same time, pictures of Brain MRIs are accompanied by noise. Eliminating existing noises can significantly impact the better segmentation and diagnosis of brain tumors. In this work, we have tried using the analysis of eigenvalues. We have used the MSVD algorithm, reducing the image noise and then using the deep neural network to segment the tumor in the images. The proposed method's accuracy was increased by 2.4% compared to using the original images. With Using the MSVD method, convergence speed has also increased, showing the proposed method's effectiveness.

Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation

Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classification and showed inferior segmentation results. In this paper, we proposed a region-based EDL segmentation framework that can generate reliable uncertainty maps and accurate segmentation results, which is robust to noise and image corruption. We used the Theory of Evidence to interpret the output of a neural network as evidence values gathered from input features. Following Subjective Logic, evidence was parameterized as a Dirichlet distribution, and predicted probabilities were treated as subjective opinions. To evaluate the performance of our model on segmentation and uncertainty estimation, we conducted quantitative and qualitative experiments on the BraTS 2020 dataset. The results demonstrated the top performance of the proposed method in quantifying segmentation uncertainty and robustly segmenting tumors. Furthermore, our proposed new framework maintained the advantages of low computational cost and easy implementation and showed the potential for clinical application.

3D Automatic Segmentation of Brain Tumor Based on Deep Neural Network and Multimodal MRI Images.

Brain tumor segmentation is an important content in medical image processing, and it is also a very common research in medicine. Due to the development of modern technology, it is very valuable to use deep learning (DL) and multimodal MRI images to study brain tumor segmentation. In order to solve the problems of low efficiency and low accuracy of brain tumor segmentation, this paper proposes DL to conduct research on multimodal MRI image segmentation, aiming to make accurate diagnosis and treatment for doctors. In addition, this paper constructs an automatic diagnosis system for brain tumors, uses GLCM and discrete wavelet transform (DWT) to extract features from MRI images, and then uses a convolutional neural network (CNN) for final diagnosis; finally, through four. The comparison of the results between the two algorithms proves that the CNN algorithm has the better processing power and higher efficiency.

Research on the magnetic resonance imaging brain tumor segmentation algorithm based on DO‐UNet

AbstractWith the social and economic development and the improvement of people's living standards, smart medical care is booming, and medical image processing is becoming more and more popular in research, of which brain tumor segmentation is an important branch of medical image processing. However, the manual segmentation method of brain tumors requires a lot of time and effort from the doctor and has a great impact on the treatment of patients. In order to solve this problem, we propose a DO‐UNet model for magnetic resonance imaging brain tumor image segmentation based on attention mechanism and multi‐scale feature fusion to realize fully automatic segmentation of brain tumors. Firstly, we replace the convolution blocks in the original U‐Net model with the residual modules to prevent the gradient disappearing. Secondly, the multi‐scale feature fusion is added to the skip connection of U‐Net to fuse the low‐level features and high‐level features more effectively. In addition, in the decoding stage, we add an attention mechanism to increase the weight of effective information and avoid information redundancy. Finally, we replace the traditional convolution in the model with DO‐Conv to speed up the network training and improve the segmentation accuracy. In order to evaluate the model, we used the BraTS2018, BraTS2019, and BraTS2020 datasets to train the improved model and validate it online, respectively. Experimental results show that the DO‐UNet model can effectively improve the accuracy of brain tumor segmentation and has good segmentation performance.

TransConver: transformer and convolution parallel network for developing automatic brain tumor segmentation in MRI images.

Medical image segmentation plays a vital role in computer-aided diagnosis (CAD) systems. Both convolutional neural networks (CNNs) with strong local information extraction capacities and transformers with excellent global representation capacities have achieved remarkable performance in medical image segmentation. However, because of the semantic differences between local and global features, how to combine convolution and transformers effectively is an important challenge in medical image segmentation. In this paper, we proposed TransConver, a U-shaped segmentation network based on convolution and transformer for automatic and accurate brain tumor segmentation in MRI images. Unlike the recently proposed transformer and convolution based models, we proposed a parallel module named transformer-convolution inception (TC-inception), which extracts local and global information via convolution blocks and transformer blocks, respectively, and integrates them by a cross-attention fusion with global and local feature (CAFGL) mechanism. Meanwhile, the improved skip connection structure named skip connection with cross-attention fusion (SCCAF) mechanism can alleviate the semantic differences between encoder features and decoder features for better feature fusion. In addition, we designed 2D-TransConver and 3D-TransConver for 2D and 3D brain tumor segmentation tasks, respectively, and verified the performance and advantage of our model through brain tumor datasets. We trained our model on 335 cases from the training dataset of MICCAI BraTS2019 and evaluated the model's performance based on 66 cases from MICCAI BraTS2018 and 125 cases from MICCAI BraTS2019. Our TransConver achieved the best average Dice score of 83.72% and 86.32% on BraTS2019 and BraTS2018, respectively. We proposed a transformer and convolution parallel network named TransConver for brain tumor segmentation. The TC-Inception module effectively extracts global information while retaining local details. The experimental results demonstrated that good segmentation requires the model to extract local fine-grained details and global semantic information simultaneously, and our TransConver effectively improves the accuracy of brain tumor segmentation.

Robustifying Deep Networks for Medical Image Segmentation.

The purpose of this study is to investigate the robustness of a commonly used convolutional neural network for image segmentation with respect to nearly unnoticeable adversarial perturbations, and suggest new methods to make these networks more robust to such perturbations. In this retrospective study, the accuracy of brain tumor segmentation was studied in subjects with low- and high-grade gliomas. Two representative UNets were implemented to segment four different MR series (T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR) into four pixelwise labels (Gd-enhancing tumor, peritumoral edema, necrotic and non-enhancing tumor, and background). We developed attack strategies based on the fast gradient sign method (FGSM), iterative FGSM (i-FGSM), and targeted iterative FGSM (ti-FGSM) to produce effective but imperceptible attacks. Additionally, we explored the effectiveness of distillation and adversarial training via data augmentation to counteract these adversarial attacks. Robustness was measured by comparing the Dice coefficients for the attacks using Wilcoxon signed-rank tests. The experimental results show that attacks based on FGSM, i-FGSM, and ti-FGSM were effective in reducing the quality of image segmentation by up to 65% in the Dice coefficient. For attack defenses, distillation performed significantly better than adversarial training approaches. However, all defense approaches performed worse compared to unperturbed test images. Therefore, segmentation networks can be adversely affected by targeted attacks that introduce visually minor (and potentially undetectable) modifications to existing images. With an increasing interest in applying deep learning techniques to medical imaging data, it is important to quantify the ramifications of adversarial inputs (either intentional or unintentional).

Study on MRI Brain Tumor Recognition and Segmentation Based on an Improved U-Net Network Model

Current malignant tumor is one of the main causes of death in Chinese residents, and the incidence of brain tumor in the population is higher. With the continuous development of modern imaging technology, especially MRI imaging technology, it provides a favorable help for doctors to assess brain tumors and choose treatment options. The accuracy of brain tumor segmentation is the key for doctors to diagnose patients’ medical condition information. Brain tumor’s structure is complex, shape is changeable and grayscale isn’t uniform, showing considerable differences in different patients. Artificial segmentation of MRI brain tumor images is time-consuming, and it is often affected by subjective differences. For the above reasons, researchers are working to develop an MRI brain tumor recognition segmentation method. This paper presents a recognition and segmentation method for MRI brain tumors, based on an improved U-Net network model. The proposed method avoids too large or too small learning rate leading to too fast or too slow convergence, and adds the BatchNormalization module to normalize the input of the input activation function of the network, thus realizing the training length, robustness, and accurately and completely dividing the brain tumor image. This paper performs detection experiments on the Kaggle+LGG Datasets, with the average classification accuracy of 99.8309%, the average IoU is 0.9935, and the average Dice coefficient is 0.9956.

AIU-Net: An Efficient Deep Convolutional Neural Network for Brain Tumor Segmentation

Automatic and accurate segmentation of brain tumors plays an important role in the diagnosis and treatment of brain tumors. In order to improve the accuracy of brain tumor segmentation, an improved multimodal MRI brain tumor segmentation algorithm based on U-net is proposed in this paper. In the original U-net, the contracting path uses the pooling layer to reduce the resolution of the feature image and increase the receptive field. In the expanding path, the up sampling is used to restore the size of the feature image. In this process, some details of the image will be lost, leading to low segmentation accuracy. This paper proposes an improved convolutional neural network named AIU-net (Atrous-Inception U-net). In the encoder of U-net, A-inception (Atrous-inception) module is introduced to replace the original convolution block. The A-inception module is an inception structure with atrous convolution, which increases the depth and width of the network and can expand the receptive field without adding additional parameters. In order to capture the multiscale features, the atrous spatial pyramid pooling module (ASPP) is introduced. The experimental results on the BraTS (the multimodal brain tumor segmentation challenge) dataset show that the dice score obtained by this method is 0.93 for the enhancing tumor region, 0.86 for the whole tumor region, and 0.92 for the tumor core region, and the segmentation accuracy is improved.

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.

Automatic Brain Tumor Segmentation Based on Cascaded Convolutional Neural Networks With Uncertainty Estimation.

Automatic segmentation of brain tumors from medical images is important for clinical assessment and treatment planning of brain tumors. Recent years have seen an increasing use of convolutional neural networks (CNNs) for this task, but most of them use either 2D networks with relatively low memory requirement while ignoring 3D context, or 3D networks exploiting 3D features while with large memory consumption. In addition, existing methods rarely provide uncertainty information associated with the segmentation result. We propose a cascade of CNNs to segment brain tumors with hierarchical subregions from multi-modal Magnetic Resonance images (MRI), and introduce a 2.5D network that is a trade-off between memory consumption, model complexity and receptive field. In addition, we employ test-time augmentation to achieve improved segmentation accuracy, which also provides voxel-wise and structure-wise uncertainty information of the segmentation result. Experiments with BraTS 2017 dataset showed that our cascaded framework with 2.5D CNNs was one of the top performing methods (second-rank) for the BraTS challenge. We also validated our method with BraTS 2018 dataset and found that test-time augmentation improves brain tumor segmentation accuracy and that the resulting uncertainty information can indicate potential mis-segmentations and help to improve segmentation accuracy.

A Glioma Segmentation Method Using CoTraining and Superpixel-Based Spatial and Clinical Constraints

Various brain tumors are very harmful to human health, and their incidence rate has risen gradually in recent years. Magnetic resonance imaging is widely used in the evaluation and treatment of brain tumors, but a radiologist’s manual segmentation of massive images is time-consuming and produces some personal bias, and so a computer-aided diagnostic system could improve the working efficiency of radiologists. In recent years, a variety of automatic segmentation methods for brain tumor images has been proposed. Compared with other methods, machine-learning-based methods are able to obtain better segmentation accuracy, but such methods require the manual labeling of lots of samples to ensure the accuracy of the segmentation. In this study, semi-supervised learning theory and image spatial and clinical a priori knowledge of brain tumors are combined to propose a new brain-tumor segmentation method that can improve the segmentation accuracy with multiple classifier collaborative training (CoTraining) under the premise of fewer labeled data. In addition, according to the prior knowledge that image adjacent pixels belong to similar classes and clinical knowledge, we used superpixel graphs to construct a spatial and clinical constraint to improve brain-tumor segmentation accuracy further. The proposed method was tested on the Brain Tumor Segmentation Challenge 2012 and 2013 datasets (BRATS 2012, 2013). The experimental results demonstrate that the proposed method was able to overcome the drawbacks of fewer training samples effectively and obtained remarkable brain-tumor segmentation performance.