Mesh free alternate directional implicit method based three dimensional super-diffusive model for benign brain tumor segmentation

2019 ◽  
Vol 77 (12) ◽  
pp. 3212-3223 ◽  
Author(s):  
Saroj Kumar Chandra ◽  
Manish Kumar Bajpai
Keyword(s):  
Brain Tumor ◽  
Three Dimensional ◽  
Implicit Method ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Diffusive Model ◽  
Mesh Free

scholarly journals SGPNet: A Three-Dimensional Multitask Residual Framework for Segmentation and IDH Genotype Prediction of Gliomas

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Wang ◽  
Yan Wang ◽  
Chunjie Guo ◽  
Shuangquan Zhang ◽  
Lili Yang
Keyword(s):  
Brain Tumor ◽  
Clinical Decision Making ◽  
Three Dimensional ◽  
Clinical Decision ◽  
Error Rates ◽  
The Cancer Genome Atlas ◽  
Classification Error ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
The Status

Glioma is the main type of malignant brain tumor in adults, and the status of isocitrate dehydrogenase (IDH) mutation highly affects the diagnosis, treatment, and prognosis of gliomas. Radiographic medical imaging provides a noninvasive platform for sampling both inter and intralesion heterogeneity of gliomas, and previous research has shown that the IDH genotype can be predicted from the fusion of multimodality radiology images. The features of medical images and IDH genotype are vital for medical treatment; however, it still lacks a multitask framework for the segmentation of the lesion areas of gliomas and the prediction of IDH genotype. In this paper, we propose a novel three-dimensional (3D) multitask deep learning model for segmentation and genotype prediction (SGPNet). The residual units are also introduced into the SGPNet that allows the output blocks to extract hierarchical features for different tasks and facilitate the information propagation. Our model reduces 26.6% classification error rates comparing with previous models on the datasets of Multimodal Brain Tumor Segmentation Challenge (BRATS) 2020 and The Cancer Genome Atlas (TCGA) gliomas’ databases. Furthermore, we first practically investigate the influence of lesion areas on the performance of IDH genotype prediction by setting different groups of learning targets. The experimental results indicate that the information of lesion areas is more important for the IDH genotype prediction. Our framework is effective and generalizable, which can serve as a highly automated tool to be applied in clinical decision making.

scholarly journals Improved U-Net3+ with Stage Residual for Brain Tumor Segmentation

2021 ◽  
Author(s):  
Chuanbo Qin ◽  
Yujie Wu ◽  
Wenbin Liao ◽  
Junying Zeng ◽  
Shufen Liang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Batch Size ◽  
Tumor Segmentation ◽  
Extraction Ability ◽  
Brain Tumor Segmentation ◽  
Residual Structure ◽  
3D Network

Abstract Background For the coding part of U-Net3+, the brain tumor feature extraction ability is insufficient, leading to insufficient feature fusion when sampling on the network and reducing the segmentation accuracy. Methods In this study, we propose an improved U-Net3+ segmentation network based on stage residual. In the encoder part, the encoder based on the stage residual structure is used to reduce the degradation problem caused by the increase in network depth and enhance the feature extraction ability of the encoder, which is convenient for full feature fusion when sampling on the network. Besides, we used a filter response normalization (FRN) layer instead of a batch normalization layer to eliminate batch size impact on the network. Based on the improved U-Net3+ two-dimensional (2D) model with stage residual, IResUnet3+ three-dimensional (3D) model is constructed. We explore appropriate methods to deal with 3D data, which achieve accurate segmentation of the 3D network. Results The experimental results showed that: the sensitivity of WT, TC, and ET increased by 1.34%, 4.6%, and 8.44%, respectively. And the Dice coefficients of ET and WT were further increased by 3.43% and 1.03%, respectively. To facilitate further research, source code can be found at: https://github.com/YuOnlyLookOne/IResUnet3Plus. Conclusion In the segmentation task of brain tumor brats2018 dataset, compared with the classical networks u-net, v-net, resunet and u-net3 +, the proposed network has smaller parameters and significantly improved accuracy.

Three-Dimensional Parallel Convolution Neural Network Brain Tumor Segmentation Based on Dilated Convolution

2020 ◽  
Vol 57 (14) ◽  
pp. 141009
Author(s):  
冯博文 Feng Bowen ◽  
吕晓琪 Lü Xiaoqi ◽  
谷宇 Gu Yu ◽  
李菁 Li Qing ◽  
刘阳 Liu Yang
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Three Dimensional ◽  
Convolution Neural Network ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Dilated Convolution

Brain Tumor Segmentation Using 3D Generative Adversarial Networks

2020 ◽  
pp. 2157002
Author(s):  
Yitong Li ◽  
Yue Chen ◽  
Y. Shi
Keyword(s):  
Brain Tumor ◽  
Treatment Options ◽  
Medical Image Analysis ◽  
Three Dimensional ◽  
Subsequent Treatment ◽  
Generative Adversarial Networks ◽  
Tumor Segmentation ◽  
High Morbidity ◽  
Brain Tumor Segmentation ◽  
Adversarial Networks

Brain tumors have high morbidity and may lead to highly lethal cancer. In clinics, accurate segmentation of tumors is the means for diagnosis and determination of subsequent treatment options. Due to the irregularity and blurring of tumor boundaries, accurately segmenting the tumor lesions has received extensive attention in medical image analysis. In view of this situation, this paper proposed a brain tumor segmentation method based on generative adversarial networks (GANs). The GAN architecture consists of a densely connected three-dimensional (3D) U-Net used for segmentation and a classification network for discrimination, both of which use 3D convolutions to fuse multi-dimensional context information. The densely connected 3D U-Net model introduces a dense connection to accelerate network convergence, extracting more detailed information. The adversarial training makes the distribution of segmentation results closer to that of labeled data, which enables the network to segment some unexpected small tumor subregions. Alternately, train two networks and finally achieve a highly accurate classification of each voxel. The experiments conducted on BraTS2017 brain tumor MRI dataset show that the proposed method has higher accuracy in brain tumor segmentation.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Brain Tumor Segmentation Using Deep Belief Networks and Pathological Knowledge

2017 ◽  
Vol 16 (2) ◽  
pp. 129-136 ◽  
Author(s):  
Tianming Zhan ◽  
Yi Chen ◽  
Xunning Hong ◽  
Zhenyu Lu ◽  
Yunjie Chen
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Brain Tumor Segmentation
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