•A novel network architecture RescueNet is proposed for brain tumor segmentation.•An unpaired GAN based training approach is proposed to train the RescueNet.•Scale-invariant post-processing algorithm ...is proposed to enhance the accuracy.•Performance of the proposed network is tested on BraTS-2015 and BraTS-2017 dataset.
Even with proper acquisition of brain tumor images, the accurate and reliable segmentation of tumors in brain is a complicated job. Automatic segmentation become possible with development of deep learning algorithms that brings plethora of solutions in this research prospect. In this paper, we designed a network architecture named as residual cyclic unpaired encoder-decoder network (RescueNet) using residual and mirroring principles. RescueNet uses unpaired adversarial training to segment the whole tumor followed by core and enhance regions in a brain MRI scan. The problem in automatic brain tumor analysis is preparing large scale labeled data for training of deep networks which is a time consuming and tedious task. To eliminate this need of paired data we used unpaired training approach to train the proposed network. Performance evaluation parameters are taken as DICE and Sensitivity measure. The experimental results are tested on BraTS 2015 and BraTS 2017 1 dataset and the result outperforms the existing methods for brain tumor segmentation. The combination of domain-specific segmentation methods and general-purpose adversarial learning loomed to leverage huge advantages for medical imaging applications and can improve the ability of automated algorithms to assist radiologists.
The most aggressive form of brain tumor is gliomas, which leads to concise life when high grade. The early detection of glioma is important to save the life of patients. MRI is a commonly used ...approach for brain tumors evaluation. However, the massive amount of data provided by MRI prevents manual segmentation in a reasonable time, restricting the use of accurate quantitative measurements in clinical practice. An automatic and reliable method is required that can segment tumors accurately. To achieve end-to-end brain tumor segmentation, a hybrid deep learning model RMU-Net is proposed. The architecture of MobileNetV2 is modified by adding residual blocks to learn in-depth features. This modified Mobile Net V2 is used as an encoder in the proposed network, and upsampling layers of U-Net are used as the decoder part. The proposed model has been validated on BraTS 2020, BraTS 2019, and BraTS 2018 datasets. The RMU-Net achieved the dice coefficient scores for WT, TC, and ET of 91.35%, 88.13%, and 83.26% on the BraTS 2020 dataset, 91.76%, 91.23%, and 83.19% on the BraTS 2019 dataset, and 90.80%, 86.75%, and 79.36% on the BraTS 2018 dataset, respectively. The performance of the proposed method outperforms with less computational cost and time as compared to previous methods.
Brain cancer is one of the most dominant causes of cancer death; the best way to diagnose and treat brain tumors is to screen early. Magnetic Resonance Imaging (MRI) is commonly used for brain tumor ...diagnosis; however, it is a challenging problem to achieve higher accuracy and performance, which is a vital problem in most of the previously presented automated medical diagnosis. In this paper, we propose a Hybrid Two-Track U-Net(HTTU-Net) architecture for brain tumor segmentation. This architecture leverages the use of Leaky Relu activation and batch normalization. It includes two tracks; each one has a different number of layers and utilizes a different kernel size. Then, we merge these two tracks to generate the final segmentation. We use the focal loss, and generalized Dice (GDL), loss functions to address the problem of class imbalance. The proposed segmentation method was evaluated on the BraTS'2018 datasets and obtained a mean Dice similarity coefficient of 0.865 for the whole tumor region, 0.808 for the core region and 0.745 for the enhancement region and a median Dice similarity coefficient of 0.883, 0.895, and 0.815 for the whole tumor, core and enhancing region, respectively. The proposed HTTU-Net architecture is sufficient for the segmentation of brain tumors and achieves highly accurate results. Other quantitative and qualitative evaluations are discussed, along with the paper. It confirms that our results are very comparable expert human-level performance and could help experts to decrease the time of diagnostic.
A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor ...area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research.
The research presents a deep CNN to segment brain tumor in MRI. Proposed architecture consists of multiple CNN layers connected in sequential order using Convolutional feature maps at peer level. Experiments on BRATS 2015 exhibit promising results.
•A novel hybrid 3D deep residual convolutional U-Net (dResU-Net) architecture is proposed for brain tumor segmentation.•Low-level features are being preserved with high-level features for better ...segmentation within the encoder part.•The proposed model is cross-validated on an external cohort dataset to evaluate the robustness and generalizability of the architecture.•The proposed model used 3D multi-modal MRI for the automatic segmentation of brain tumor sub-regions.•The proposed study used a combined loss function based on Dice and Focal loss.
Glioma is the most prevalentand dangerous type of brain tumor which can be life-threatening when its grade is high. The early detection of these tumors can improve and save the life of the patients. The automatic segmentation of brain tumor from magnetic resonance imaging (MRI) plays a vital role in treatment planning and timely diagnosis. Automatic segmentation is a challenging task due to the massive amount of information provided by MRI and the variation in the location and size of the tumor. Therefore, a reliable and authentic method to segment the tumorous region from healthy tissues accurately is an open challenge in the field of deep learning-based medical image analysis. This research paper presents an end-to-end framework for automatic 3D Brain Tumor Segmentation (BTS). The proposed model is a hybrid of the deep residual network and U-Net model (dResU-Net). The residual network is used as an encoder in the proposed architecture with the decoder of the U-Net model to handle the issue of vanishing gradient. The proposed model is designed to take advantage from low-level and high-level features simultaneously for making the prediction. In addition, shortcut connections are employed between residual network to preserve low-level features at each level. Furthermore, skip connections between residual and convolutional blocks in the proposed architecture are used to accelerate the training process. The proposed architecture achieved promising results with the average dice score for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) on the BraTS 2020 dataset of 0.8357, 0.8660, and 0.8004, respectively. To demonstrate the robustness of the proposed model in real-world clinical settings, validation of the trained model on an external cohort is performed on randomly selected 50 patients of the BraTS 2021 benchmark dataset. The achieved dice scores on the external cohort are 0.8400, 0.8601, and 0.8221 for TC, WT, and ET, respectively. The comparison of results of the proposed approach with the state-of-the-art techniques indicates that dResU-Net can significantly improve the segmentation performance of brain tumor sub-regions.
Segmentation of brain tumor from 3D images is one of the most important and difficult tasks in the field of medical image processing as a manual human-assisted categorization can result in incorrect ...prediction and diagnosis. Furthermore, it is a difficult process when there is a huge amount of data to assist. Extracting brain tumour regions from MRI images becomes challenging due to the great variety of appearances of brain tumours and how similar they are to normal tissues. In this paper, we have designed modified U-Net architecture under a deep-learning framework for the detection and segmentation of brain tumors from MRI images. The applied model has been evaluated on genuine images provided by Medical Image Computing and Computer-Assisted Interventions BRATS 2020 datasets. Test accuracy of 99.4% has been achieved using the above-mentioned dataset. A comparative review with other papers shows our model using U-Net performs better than other deep learning-based models.
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