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Brain SegNet: 3D local refinement network for brain lesion segmentation

Xiaojun Hu, Weijian Luo, Jiliang Hu, Sheng Guo, Weilin Huang, Matthew R. Scott, Roland Wiest, Michael Dahlweid, Mauricio Reyes
2020 BMC Medical Imaging  
We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion.  ...  We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.  ...  brain segmentation network (Brain SegNet) for brain lesion segmentation from MRIs.  ... 
doi:10.1186/s12880-020-0409-2 pmid:32046685 fatcat:f77ssgr2sjfuvjzczbebaiqlhu

Brain SegNet: 3D local refinement network for brain lesion segmentation

Xiaojun Hu, Weijian Luo, Jiliang Hu, Sheng Guo, Weilin Huang, Matthew R Scott, Roland Wiest, Michael Dahlweid, Mauricio Reyes
We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion.  ...  MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome.  ...  brain segmentation network (Brain SegNet) for brain lesion segmentation from MRIs.  ... 
doi:10.7892/boris.140719 fatcat:zzdpkfwiw5hkvjyh3gewr6e67y

Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation

Chaitra Dayananda, Jae-Young Choi, Bumshik Lee
2021 Sensors  
The proposed method was evaluated on publicly accessible datasets for brain tissue segmentation.  ...  The proposed global attention module refines the feature extraction and improves the representational power of the convolutional neural network.  ...  CNNs for the segmentation of brain MRI images.  ... 
doi:10.3390/s21103363 pmid:34066042 fatcat:qlmzlwho3zfjnc4y24fnjxgrlu

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates

Gengyan Zhao, Fang Liu, Jonathan A. Oler, Mary E. Meyerand, Ned H. Kalin, Rasmus M. Birn
2018 NeuroImage  
Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume.  ...  The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine.  ...  Brain Imaging and Behavior, and the Wisconsin National Primate Research Center.  ... 
doi:10.1016/j.neuroimage.2018.03.065 pmid:29604454 pmcid:PMC6095475 fatcat:jmf4m5sadbaelk4dyvwrnclgmm

Brain Image Segmentation in Recent Years: A Narrative Review

Ali Fawzi, Anusha Achuthan, Bahari Belaton
2021 Brain Sciences  
This paper aims to present a critical review of the recent trend in segmentation and classification methods for brain magnetic resonance images.  ...  This has indirectly led to an increased demand for automated brain segmentation solutions to assist medical experts in early diagnosis and treatment interventions.  ...  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

Multi-modality Reconstruction Attention and Difference Enhancement Network for Brain MRI Image Segmentation

Xiangfen Zhang, Yan Liu, Qingyi Zhang, Feiniu Yuan
2022 IEEE Access  
An important prerequisite for brain disease diagnosis is to segment brain tissues of Magnetic Resonance Imaging (MRI) into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF).  ...  for weighting the three inputs.  ...  contextual information and refine the detailed features of the brain MRI images.  ... 
doi:10.1109/access.2022.3156898 fatcat:kob2xxgpmvbfplodcqn7v5nb7i

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.  ...  The results do highlight that model-driven classical statistics and data-driven deep learning is a potent combination for developing automated systems in clinical oncology.  ...  In the realm of tumor segmentation, Kamnitsas et al. (2017c) used a 3D CNN model in conjunction with a 3D fully connected CRF for segmenting brain lesions from 3D medical scans.  ... 
arXiv:2103.05529v1 fatcat:iqu5ix5tgre6pnokdmoejywh74

Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

Hafiz Zia Ur Rehman, Hyunho Hwang, Sungon Lee
2020 Applied Sciences  
Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which  ...  brain.  ...  [93] suggested that 3D-UNet, the general 3D segmentation network, could be used for skull-stripping problems.  ... 
doi:10.3390/app10051773 fatcat:nwp2z2y4jzgoxinv7sfppbfrfa

Novel Volumetric Sub-region Segmentation in Brain Tumors

Subhashis Banerjee, Sushmita Mitra
2020 Frontiers in Computational Neuroscience  
A novel deep learning based model called Multi-Planar Spatial Convolutional Neural Network (MPS-CNN) is proposed for effective, automated segmentation of different sub-regions viz. peritumoral edema (ED  ...  The MPS-CNN is trained and validated on the recent Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018 dataset.  ...  Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Trans. Med.  ... 
doi:10.3389/fncom.2020.00003 pmid:32038216 pmcid:PMC6993215 fatcat:7clvk46kz5cptbwtusucyh76fm


2021 Journal of Critical Reviews  
Deep learning models such as the convolutionary neural network have been widely used in 3D biomedical segmentation and have achieved state-of-the-art performance.In this research, saliency based deep features  ...  Tumor segmentation is the primary and tedious task for the clinical experts. Computer Aided Design is the only solution which identifies the tumor very accurately with less time.  ...  For the challenging task of brain lesion segmentation a dual pathway is developed, 11-layer deep, 3D CNN.  ... 
doi:10.31838/jcr.07.19.71 fatcat:7xb5xyb5kzfgtksmocftp5qeky

Aggregation-and-Attention Network for brain tumor segmentation

Chih-Wei Lin, Yu Hong, Jinfu Liu
2021 BMC Medical Imaging  
Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation.  ...  However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect.  ...  SegNet, RefineNet, and CE-Net further refined the boundary contour of different tissues in glioma but did not segment the scattered edema area.  ... 
doi:10.1186/s12880-021-00639-8 pmid:34243703 pmcid:PMC8267236 fatcat:6xu6xk3xrvh3tcsodriwjezttm

A Deep Multi-Task Learning Framework for Brain Tumor Segmentation

He Huang, Guang Yang, Wenbo Zhang, Xiaomei Xu, Weiji Yang, Weiwei Jiang, Xiaobo Lai
2021 Frontiers in Oncology  
However, manually segmenting brain tumors from MRI requires a lot of time and energy for doctors, which affects the implementation of follow-up diagnosis and treatment plans.  ...  However, brain tumors are easily confused with strokes and serious imbalances between classes make brain tumor segmentation one of the most difficult tasks in MRI segmentation.  ...  Badrinarayanan V. et al. proposed a new semantic pixel segmentation network structure SegNet, which is based on the DeconvNet.  ... 
doi:10.3389/fonc.2021.690244 pmid:34150660 pmcid:PMC8212784 fatcat:z6qi27vfybbf3arvadbxertnaa

MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer's Disease: A Survey

Nagaraj Yamanakkanavar, Jae Young Choi, Bumshik Lee
2020 Sensors  
We aim to provide an outline of current deep learning-based segmentation approaches for the quantitative analysis of brain MRI for the diagnosis of AD.  ...  Here, we report how convolutional neural network architectures are used to analyze the anatomical brain structure and diagnose AD, discuss how brain MRI segmentation improves AD classification, describe  ...  Dolz [70] CNN Anatomical segmentation: 3D CNN architecture for the segmentation of subcortical MRI brain structure.  ... 
doi:10.3390/s20113243 pmid:32517304 fatcat:bfd5dffy4vbktnsoroi5o2je2a

BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder–Decoder Network

Mobeen Ur Rehman, SeungBin Cho, Jeehong Kim, Kil To Chong
2021 Diagnostics  
In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions.  ...  The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions.  ...  The Brain-SegNet attained a dice score of 0.903, 0.872, and 0.849 for the whole, core, and enhancing tumor, respectively. A segmentation improvement is observed for all the classes.  ... 
doi:10.3390/diagnostics11020169 pmid:33504047 pmcid:PMC7911842 fatcat:kki6hptg25gbpll7nzjaecaat4

An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks

Nagesh Subbanna, Matthias Wilms, Anup Tuladhar, Nils D. Forkert
2021 Sensors  
For the development and evaluation of model inversion attacks, the public LPBA40 database consisting of 40 brain MRI scans with corresponding segmentations of the gyri and deep grey matter brain structures  ...  However, the feasibility of this attack type has not been investigated for complex 3D medical images.  ...  Segmentation of brain structures as the task being analyzed instead of ischaemic stroke lesion segmentation. 4.  ... 
doi:10.3390/s21113874 fatcat:2fe2dncmqrhgpjhkdnlkayweym
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