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A deep learning model integrating FCNNs and CRFs for brain tumor segmentation

Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yazhuo Zhang, Yong Fan
2018 Medical Image Analysis  
) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices.  ...  Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a  ...  Fig. 3 . 3 Flowchart of the proposed deep learning model integrating FCNNs and CRFs for brain tumor segmentation.  ... 
doi:10.1016/ pmid:29040911 pmcid:PMC6029627 fatcat:ni5wpcj3qbhorhb6qppmi7ihoa


Wu Deng, Qinke Shi, Miye Wang, Bing Zheng, Ning Ning
2020 IEEE Access  
A brain tumor segmentation approach is developed based on efficient, deep learning techniques implemented in a unified system to achieve the appearance and spatial accuracy outcomes through Conditional  ...  In general, 3 segmentation models have been trained using axial-, coronary-and sagittal image patches and slices, Further assembled into brain tumor segments using a voting fusion technique and it can  ...  DEEP LEARNING BASED HCNN AND CRF-RRNN MODEL FOR BRAIN TUMOUR SEGMENTATION The method proposed for segmentation of brain tumors comprises 4 main steps: preprocessing, image slices segmented with integrated  ... 
doi:10.1109/access.2020.2966879 fatcat:fa4pjx6wavehlj2c4xv3ayqhsq

RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields

Gaoxiang Chen, Qun Li, Fuqian Shi, Islem Rekik, Li Wang, Zhifang Pan
2020 NeuroImage  
In this paper, we propose a two-stage supervised learning framework for automatic brain lesion segmentation.  ...  Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy.  ...  Taking brain tumor segmentation as an example, features based on intensity, texture, and symmetrical information can help classify voxels into 'tumor' and other tissue types.  ... 
doi:10.1016/j.neuroimage.2020.116620 pmid:32057997 fatcat:hrpswp3ucncd3onnazqhmhdmra

Brain Image Segmentation in Recent Years: A Narrative Review

Ali Fawzi, Anusha Achuthan, Bahari Belaton
2021 Brain Sciences  
From this review, it is found that deep learning-based and hybrid-based metaheuristic approaches are more efficient for the reliable segmentation of brain tumors.  ...  This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images.  ...  Acknowledgments: The authors would thank the Ministry of Higher Education Malaysia And Universiti Sains Malaysia for providing the infrastructures and supports to complete this work.  ... 
doi:10.3390/brainsci11081055 fatcat:cdie3nuxzzfevoynik3iqtenli

Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation [article]

K. Ruwani M. Fernando, Chris P. Tsokos
2021 arXiv   pre-print
In this study, we critically review major statistical and deep learning models and their applications in brain imaging research with a focus on MRI-based brain tumor segmentation.  ...  Early methods of brain cancer characterization predominantly relied upon statistical modeling of neuroimaging data.  ...  The parameters in brain tissue segmentation may include intensity, bias correction, and registration parameters.  ... 
arXiv:2103.05529v1 fatcat:iqu5ix5tgre6pnokdmoejywh74

Brain Tumor Segmentation using Multi-View Attention based Ensemble Network

Noreen Mushtaq, Arfat Ahmad Khan, Faizan Ahmed Khan, Muhammad Junaid Ali, Malik Muhammad Ali Shahid, Chitapong Wechtaisong, Peerapong Uthansakul
2022 Computers Materials & Continua  
Deep learning-based brain tumor segmentation techniques proved best among available segmentation techniques.  ...  Astrocytoma IV or glioblastoma is one of the fatal and dangerous types of brain tumors. Early detection of brain tumor increases the survival rate and helps in reducing the fatality rate.  ...  Deep learning-based brain tumor segmentation techniques proved best among available segmentation techniques.  ... 
doi:10.32604/cmc.2022.024316 fatcat:7bv7jktebnc5jovca3jh4q3ywu

Fully connected CRF with data-driven prior for multi-class brain tumor segmentation

Haocheng Shen, Jianguo Zhang
2017 2017 IEEE International Conference on Image Processing (ICIP)  
In this paper, we present a novel method for brain tumor segmentation in MR images based on fully-connected CRF (FC-CRF) model that establishes pairwise potentials on all pairs of pixels in the images.  ...  We employ a hierarchical approach to differentiate different structures of tumor and further formulate a FC-CRF model with learned data-driven prior knowledge of tumor core.  ...  Acknowledgments This work was supported partially by the National Natural Science Foundation of China (No. 61628212).  ... 
doi:10.1109/icip.2017.8296577 dblp:conf/icip/ShenZ17 fatcat:rr7jezp7nfbrpeicyxkppakdzq

Within-brain classification for brain tumor segmentation

Mohammad Havaei, Hugo Larochelle, Philippe Poulin, Pierre-Marc Jodoin
2015 International Journal of Computer Assisted Radiology and Surgery  
Purpose: In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem.  ...  We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain.  ...  We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain.  ... 
doi:10.1007/s11548-015-1311-1 pmid:26530300 fatcat:dyuzd52ed5hbzaa6hbi6v7ikk4

Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation [article]

Zhanghexuan Ji, Yan Shen, Chunwei Ma, Mingchen Gao
2019 arXiv   pre-print
In this paper, we use only two kinds of weak labels, i.e., scribbles on whole tumor and healthy brain tissue, and global labels for the presence of each substructure, to train a deep learning model to  ...  The recent state-of-the-art deep learning methods have significantly improved brain tumor segmentation.  ...  Methods Our scribble-based hierarchical weakly supervised model for brain tumor segmentation consists of two phases: 1.  ... 
arXiv:1911.02014v1 fatcat:osaxye7qdrc3vdzosyn3y4nify

Automated glioma detection and segmentation using graphical models

Zhe Zhao, Guan Yang, Yusong Lin, Haibo Pang, Meiyun Wang, Jie Tian
2018 PLoS ONE  
This paper presents a probabilistic method for detection and segmentation between abnormal tissue regions and brain tumour (tumour core and edema) portions from Magnetic Resonance Imaging (MRI).  ...  and glioma segmentation scheme. ℓ 1 -regularization techniques are applied to learn the appropriate structure for modeling graphical models.  ...  [23] segment tumor and healthy tissues including sub-compartments based on SVM classification with integrated hierarchical CRF regularization.  ... 
doi:10.1371/journal.pone.0200745 pmid:30130371 fatcat:vzmsb2pnhrhy7ieaswedc7mx3a

Deep Learning Based Brain Tumor Segmentation: A Survey [article]

Zhihua Liu, Lei Tong, Zheheng Jiang, Long Chen, Feixiang Zhou, Qianni Zhang, Xiangrong Zhang, Yaochu Jin, Huiyu Zhou
2021 arXiv   pre-print
A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results.  ...  Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions.  ...  Fig. 4 . 4 A taxonomy of this survey for deep learning based brain tumor segmentation.  ... 
arXiv:2007.09479v3 fatcat:vdbpwfdsorfudkvnvottexd7je

Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization [chapter]

Stefan Bauer, Lutz-P. Nolte, Mauricio Reyes
2011 Lecture Notes in Computer Science  
Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer.  ...  We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization  ...  A visual inspection of the images in figure 2 shows that the segmentation results for healthy tissues as well as for tumor tissues appear much noisier when no CRF regularization is applied and inconsistencies  ... 
doi:10.1007/978-3-642-23626-6_44 fatcat:tix33m2y3ffobks2fqzi2f7op4

Brain Tumor Segmentation Using Multi-cascaded Convolutional Neural Networks and Conditional Random Field

Kai Hu, Qinghai Gan, Yuan Zhang, Shuhua Deng, Fen Xiao, Wei Huang, Chunhong Cao, Xieping Gao
2019 IEEE Access  
Accurate segmentation of brain tumor is an indispensable component for cancer diagnosis and treatment.  ...  In this paper, we propose a novel brain tumor segmentation method based on multicascaded convolutional neural network (MCCNN) and fully connected conditional random fields (CRFs).  ...  The authors would also like to thank the anonymous reviewers for their insightful comments, which have greatly helped to improve the quality of this paper.  ... 
doi:10.1109/access.2019.2927433 fatcat:daouc7z6hjgfzces4kwze4iawy

Context Aware 3D CNNs for Brain Tumor Segmentation [chapter]

Siddhartha Chandra, Maria Vakalopoulou, Lucas Fidon, Enzo Battistella, Théo Estienne, Roger Sun, Charlotte Robert, Eric Deutsch, Nikos Paragios
2019 Lecture Notes in Computer Science  
In this work we propose a novel deep learning based pipeline for the task of brain tumor segmentation.  ...  that uses the V-Net deep learning architecture to output dense, per voxel class probabilities, and (iii) a prediction refinement stage that uses a Conditional Random Field (CRF) with a bilateral filtering  ...  for the accurate segmentation of brain tumors.  ... 
doi:10.1007/978-3-030-11726-9_27 fatcat:mcf3epaz7ned3nc7jgqce3jdu4

Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF

Zeju Li, Yuanyuan Wang, Jinhua Yu, Zhifeng Shi, Yi Guo, Liang Chen, Ying Mao
2017 Journal of Healthcare Engineering  
It proved that our method could produce better results for the segmentation of low-grade gliomas.  ...  tumor, namely, glioma.  ...  Acknowledgments This work was supported by the National Basic Research Program of China (2015CB755500) and the National Natural Science Foundation of China (11474071).  ... 
doi:10.1155/2017/9283480 pmid:29065666 pmcid:PMC5485483 fatcat:74m4ouulrjcvhijs7ztnhtq3b4
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