scholarly journals A Deep-Learning Model with Learnable Group Convolution and Deep Supervision for Brain Tumor Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hengxin Liu ◽  
Qiang Li ◽  
I-Chi Wang
Keyword(s):  
Automatic Segmentation ◽  
Complex Model ◽  
Treatment Manual ◽  
Tumor Segmentation ◽  
Feature Maps ◽  
Output Stage ◽  
Convolutional Network ◽  
Brain Tumor Segmentation ◽  
Proposed Model ◽  
Network Output

The segmentation of brain tumors in medical images is a crucial step of clinical treatment. Manual segmentation is time consuming and labor intensive, and existing automatic segmentation methods suffer from issues such as numerous parameters and low precision. To resolve these issues, this study proposes a learnable group convolution-based segmentation method that replaces convolution in the feature extraction stage with learnable group convolution, thereby reducing the number of convolutional network parameters and enhancing communication between convolution groups. To improve utilization of the feature maps, we added a skip connection structure between learnable group convolution modules, which increased segmentation precision. We used deep supervision to combine output images in the network output stage to reduce overfitting and enhance the recognition capabilities of the network. We tested the proposed algorithm model using the open BraTS 2018 dataset. The experiment results revealed that the proposed model is superior to 3D U-Net and DMFNet and has better segmentation results for tumor cores than No New-Net and NVDLMED, the winning methods in the BraTS 2018 challenge. The segmentation precision of the proposed method with regard to whole tumors, enhancing tumors, and tumor cores was 90.25%, 80.36%, and 86.20%. Furthermore, the proposed method uses fewer parameters and a less complex model.

scholarly journals A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

2021 ◽  
Author(s):  
Rupal Agravat ◽  
Mehul Raval
Keyword(s):  
Overall Survival ◽  
Brain Tumor ◽  
Automatic Segmentation ◽  
High Volume ◽  
Physical Symptoms ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Neural Network Models ◽  
Brain Tumor Segmentation ◽  
Overall Survival Prediction

<div>Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results.</div><div>The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.</div>

scholarly journals Brain Tumor Segmentation in MRI Images using Convolution Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 2051-2054
Keyword(s):  
Brain Tumor ◽  
Automatic Segmentation ◽  
Important Task ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Medical Issues ◽  
Current Scenario ◽  
Exact Position ◽  
The Brain

Medical image processing is an important task in current scenario as more and more humans are diagnosed with various medical issues. Brain tumor (BT) is one of the problems that is increasing at a rapid rate and its early detection is important in increasing the survival rate of humans. Detection of tumor from Magnetic Resonance Image (MRI) of brain is very difficult when done manually and also time consuming. Further the tumors assume different shapes and may be present in any portion of the brain. Hence identification of the tumor poses an important task in the lives of human and it is necessary to identify its exact position in the brain and the affected regions. The proposed algorithm makes use of deep learning concepts for automatic segmentation of the tumor from the MRI brain images. The algorithm is implemented using MATLAB and an accuracy of 99.1% is achieved.

Automated Early Detection of Astrocytomas Tumor Based on Optimized Adaptive Cluster with Super Pixel Model

2021 ◽  
Vol 30 (06n08) ◽  
Author(s):  
V. K. Deepak ◽  
R. Sarath
Keyword(s):  
Brain Tumor ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Grey Wolf ◽  
Dice Coefficient ◽  
Grey Wolf Optimization ◽  
Brain Tumor Segmentation ◽  
Better Than

In the medical image-processing field brain tumor segmentation is aquintessential task. Thereby early diagnosis gives us a chance of increasing survival rate. It will be way much complex and time consuming when comes to processing large amount of MRI images manually, so for that we need an automatic way of brain tumor image segmentation process. This paper aims to gives a comparative study of brain tumor segmentation, which are MRI-based. So recent methods of automatic segmentation along with advanced techniques gives us an improved result and can solve issue better than any other methods. Therefore, this paper brings comparative analysis of three models such as Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP) and Grey Wolf Optimization based ACSP (GWO_ACSP) and these are tested on CANCER IMAGE ACHRCHIEVE which is a preparation information base containing High Grade and Low-Grade astrocytoma tumors. Here boundaries including Accuracy, Dice coefficient, Jaccard score and MCC are assessed and along these lines produce the outcomes. From this examination the test consequences of Grey Wolf Optimization based ACSP (GWO_ACSP) gives better answer for mind tumor division issue.

A Deep Adaptive Convolutional Network for Brain Tumor Segmentation from Multimodal MR Images

2019 ◽  
Author(s):  
Palash Ghosal ◽  
Shanmukha Reddy ◽  
Charan Sai ◽  
Vikas Pandey ◽  
Jayasree Chakraborty ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Convolutional Network ◽  
Brain Tumor Segmentation

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

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

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

scholarly journals Multi-Task Deep Supervision on Attention R2U-Net for Brain Tumor Segmentation

2021 ◽  
Vol 11 ◽  
Author(s):  
Shiqiang Ma ◽  
Jijun Tang ◽  
Fei Guo
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Computational Cost ◽  
Medical Image Segmentation ◽  
Validation Dataset ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Proposed Model ◽  
Fusion Methods ◽  
Tumor Area

Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor. However, simple deep learning models are difficult to locate the tumor area and obtain accurate segmentation boundaries. In order to solve the problems above, we propose a 2D end-to-end model of attention R2U-Net with multi-task deep supervision (MTDS). MTDS can extract rich semantic information from images, obtain accurate segmentation boundaries, and prevent overfitting problems in deep learning. Furthermore, we propose the attention pre-activation residual module (APR), which is an attention mechanism based on multi-scale fusion methods. APR is suitable for a deep learning model to help the network locate the tumor area accurately. Finally, we evaluate our proposed model on the public BraTS 2020 validation dataset which consists of 125 cases, and got a competitive brain tumor segmentation result. Compared with the state-of-the-art brain tumor segmentation methods, our method has the characteristics of a small parameter and low computational cost.

scholarly journals Brain Tumor Segmentation with Multi-Path U-Net with Residual Extended Skip Connections

2021 ◽  
Author(s):  
Batuhan Sözer ◽  
Alperen Sözer ◽  
Mustafa Çağlar Şahin ◽  
Kerem Nernekli ◽  
Şule Eylem Erdoğan ◽  
...  
Keyword(s):  
Brain Tumors ◽  
Diagnostic Techniques ◽  
Path Model ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Brain Tumor Segmentation ◽  
Proposed Model ◽  
The Brain ◽  
Ct And Mri ◽  
Segmentation Models

<p>Early diagnosis of brain tumors is extremely important, and shortening the interval between the acquisition of MRI images and reporting of the results is critical for patients. In the diagnosis of brain tumors, CT and MRI are some of the core diagnostic techniques used today. Our main goal is to reduce the workload of radiologists by developing a neural network that segments MRI images of the brain so we propose a multi-path segmentation algorithm based on U-Net architecture that uses residual extended skip blocks. Our proposed model is trained and tested with Gazi Brains 2020 Dataset. We evaluated the results using the dice similarity coefficient and compared the results with other segmentation algorithms and saw that our proposed model has comparatively better results. Our proposed model is using T1-Weighted, T2-Weighted, and Flair MRI images together as inputs, whereas other segmentation models, are using T2-Weighted or Flair MRI images as input. Implementation of the model and trained models are available at </p> <p><b>https://github.com/batuhansozer/brain-segmentation-with-novel-multi-path-model</b></p>

scholarly journals Development of Deep Learning Algorithm for Brain Tumor Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 2800-2803
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Learning Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Human Errors ◽  
Deep Learning Algorithm ◽  
Proposed Model ◽  
A New Technique

Medical imaging is an emerging field in engineering. As traditional way of brain tumor analysis, MRI scanning is the way to identify brain tumor. The core drawback of manual MRI studies conducted by surgeons is getting manual visual errorswhich can lead toofa false identification of tumor boundaries. To avoid such human errors, ultra age engineering adopted deep learning as a new technique for brain tumor segmentation. Deep learning convolution network can be further developed by means of various deep learning models for better performance. Hence, we proposed a new deep learning algorithm development which can more efficiently identifies the types of brain tumors in terms of level of tumor like T1, T2, and T1ce etc. The proposed system can identify tumors using convolution neural network(CNN) which works with the proposed algorithm “Sculptor DeepCNet”. The proposed model can be used by surgeons to identify post-surgical remains (if any) of brain tumors and thus proposed research can be useful for ultra-age neural surgical image assessments. This paper discusses newly developed algorithm and its testing results.

scholarly journals A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

2021 ◽  
Author(s):  
Rupal Agravat ◽  
Mehul Raval
Keyword(s):  
Overall Survival ◽  
Brain Tumor ◽  
Automatic Segmentation ◽  
High Volume ◽  
Physical Symptoms ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Neural Network Models ◽  
Brain Tumor Segmentation ◽  
Overall Survival Prediction

<div>Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results.</div><div>The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.</div>

Sign in / Sign up

Export Citation Format

Share Document