Filters








673 Hits in 5.3 sec

Ensembles of Convolutional Neural Networks for Survival Time Estimation of High-Grade Glioma Patients from Multimodal MRI

Kaoutar Ben Ahmed, Lawrence O. Hall, Dmitry B. Goldgof, Robert Gatenby
2022 Diagnostics  
Ensembles of snapshots of three dimensional (3D) deep convolutional neural networks (CNN) are applied to Magnetic Resonance Image (MRI) data to predict survival time of high-grade glioma patients.  ...  The BraTS (Brain Tumor Image Segmentation) 2019 training dataset was used in this work. Each patient case consisted of three MRI sequences (T1CE, T2, and FLAIR).  ...  the ensemble of five CNNs (convolutional neural networks) using the 3D T1CE sequence.  ... 
doi:10.3390/diagnostics12020345 pmid:35204436 pmcid:PMC8871067 fatcat:dzu456kgbjgszfumdsuwj6yxmi

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
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.  ...  More than 100 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality  ...  [19] reported a survey focusing on the use of deep convolutional neural networks for brain image analysis. This survey only highlights the usage of deep convolutional neural networks.  ... 
arXiv:2007.09479v3 fatcat:vdbpwfdsorfudkvnvottexd7je

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Muhammad Waqas Nadeem, Mohammed A. Al Ghamdi, Muzammil Hussain, Muhammad Adnan Khan, Khalid Masood Khan, Sultan H. Almotiri, Suhail Ashfaq Butt
2020 Brain Sciences  
Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification  ...  A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci10020118 pmid:32098333 pmcid:PMC7071415 fatcat:wofq4puvcbemlconbz6carsf2y

Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation [article]

Carlos A. Silva, Adriano Pinto, Sérgio Pereira, Ana Lopes
2021 arXiv   pre-print
Gliomas are among the most aggressive and deadly brain tumors. This paper details the proposed Deep Neural Network architecture for brain tumor segmentation from Magnetic Resonance Images.  ...  The architecture consists of a cascade of three Deep Layer Aggregation neural networks, where each stage elaborates the response using the feature maps and the probabilities of the previous stage, and  ...  The first stage receives as input multi-sequence MRI images of size 120×120, generating a first rough segmentation of the brain tumor.  ... 
arXiv:2101.00490v1 fatcat:4ghtfc4mf5avfhwi7ce4dtdify

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
Deep Neural Network and Statistical Shape Model for Pancreas Segmentation 327 One-pass Multi-task Convolutional Neural Networks for Efficient Brain Tumor Segmentation 329 MRI Measurement of Placental  ...  Synthetic CT Generation Using Multi-view Deep Convolutional Neural Networks 567 Deep learning with synthetic diffusion MRI data for free-water elimination in glioblastoma cases 568 3D Deep Convolutional  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification [article]

Sachin Gupta, Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal
2021 arXiv   pre-print
Motivated by the deep learning based computer-aided-diagnosis systems, this paper proposes multi-task attention guided encoder-decoder network (MAG-Net) to classify and segment the brain tumor regions  ...  Brain tumor is the most common and deadliest disease that can be found in all age groups. Generally, MRI modality is adopted for identifying and diagnosing tumors by the radiologists.  ...  However, for any radiologist, identification and segmentation of brain tumor via multi-sequence MRI scans for diagnosis, monitoring, and treatment, are complex  ... 
arXiv:2107.12321v2 fatcat:gajirvwx5nbvhcqhvzhblog74u

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  multiparametric magnetic resonance images 10134 06 Quantification of oxygen changes in the placenta from BOLD MR image sequences LUNG I 07 Cascade of convolutional neural networks for lung texture  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

Brain Tumour Image Segmentation Using Deep Networks

Mahnoor Ali, Syed Omer Gilani, Asim Waris, Kashan Zafar, Mohsin Jamil
2020 IEEE Access  
Extensively used for biomedical image segmentation, Convolutional Neural Networks have significantly improved the state-of-the-art accuracy on the task of brain tumour segmentation.  ...  As gliomas are malignant and heterogeneous, efficient and accurate segmentation techniques are used for the successful delineation of tumours into intra-tumoural classes.  ...  Extensively used for biomedical image segmentation, the Deep Convolutional Neural Networks have carved out a niche for achieving the state of the art accuracy on the task of brain tumour segmentation  ... 
doi:10.1109/access.2020.3018160 fatcat:veahn632a5allkot6qxc4e72uu

Glioma Classification Using Deep Radiomics

Subhashis Banerjee, Sushmita Mitra, Francesco Masulli, Stefano Rovetta
2020 SN Computer Science  
In this paper, we thoroughly investigate the power of deep convolutional neural networks (ConvNets) for classification of brain tumors using multi-sequence MR images.  ...  Therefore, development of automated systems for the detection and prediction of the grade of tumors based on MRI data becomes necessary for assisting doctors in the framework of augmented intelligence.  ...  Fig. 7 a Four sequences of an MRI slice from a sample HGG patient from TCIA [12] .  ... 
doi:10.1007/s42979-020-00214-y fatcat:mjjikll4dvgcdoykzu7euxa3wm

Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation

Kuan-Lun Tseng, Yen-Liang Lin, Winston Hsu, Chung-Yang Huang
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Deep learning models such as convolutional neural network have been widely used in 3D biomedical segmentation and achieve state-of-the-art performance.  ...  In addition, we exploit convolutional LSTM to model a sequence of 2D slices, and jointly learn the multi-modalities and convolutional LSTM in an end-to-end manner.  ...  These methods use convolution neural network (CNN) to extract deep representations and up-sample the low-resolution feature maps to produce the dense prediction results.  ... 
doi:10.1109/cvpr.2017.398 dblp:conf/cvpr/TsengLHH17 fatcat:un25cfmk6zegdmnwl4fdljoaqq

Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation [article]

Kuan-Lun Tseng, Yen-Liang Lin, Winston Hsu, Chung-Yang Huang
2017 arXiv   pre-print
Deep learning models such as convolutional neural net- work have been widely used in 3D biomedical segmentation and achieve state-of-the-art performance.  ...  In addition, we exploit convolutional LSTM to model a sequence of 2D slices, and jointly learn the multi-modalities and convolutional LSTM in an end-to-end manner.  ...  These methods use convolution neural network (CNN) to extract deep representations and up-sample the low-resolution feature maps to produce the dense prediction results.  ... 
arXiv:1704.07754v1 fatcat:xvyzjpdosvawhkdf4bydorepwm

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 proposed MPS-CNN is found to perform the best (based on leaderboard scores) for ET and TC segmentation tasks, in terms of both the quantitative measures (viz. Dice and Hausdorff).  ...  We have developed a deep learning based model called Multi-Planar Spatial Convolutional Neural Network (MPS-CNN), for the automated segmentation of brain tumors from multi-modal MR images.  ... 
doi:10.3389/fncom.2020.00003 pmid:32038216 pmcid:PMC6993215 fatcat:7clvk46kz5cptbwtusucyh76fm

BRAIN TUMOUR CLASSIFICATION INTO HIGH GRADE & LOW-GRADE GLIOMAS: A COMPARITIVE STUDY

Sonam Saluja, Dr. Munesh Chandra
2021 Zenodo  
The Precise Grading Of Gliomas Has Therapeutic Implications For Diagnosis, Surveillance, And Prognostic Procedures.  ...  With The Rapid Development In Bio Imaging Technology, Much Emphasis Has Been Placed On The Automation Of MRI-Based Brain Tumour Identification, Characterization, And Diagnostic Systems.  ...  Using A VGG-19 Deep Convolutional Neural Network, (Ahammed Muneer Et Al. 2019)Introduces Automated Glioma Tumour Grading.  ... 
doi:10.5281/zenodo.6400158 fatcat:pgyxlyxa5rhmpctse2ro4emc3m

Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation

Yue Zhao, Xiaoqiang Ren, Kun Hou, Wentao Li
2021 Symmetry  
applied in our paper to solve the problem of brain tumor segmentation, including a 3D recurrent unit and 3D multi-fiber unit.  ...  In this paper, we present an efficient semantic segmentation 3D recurrent multi-fiber network (RMFNet), which is based on encoder–decoder architecture to segment the brain tumor accurately. 3D RMFNet is  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/sym13020320 fatcat:j3odnadij5bgvjbynu2nobrbve

Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning

Li Sun, Songtao Zhang, Hang Chen, Lin Luo
2019 Frontiers in Neuroscience  
Here we present a deep learning-based framework for brain tumor segmentation and survival prediction in glioma, using multimodal MRI scans.  ...  Gliomas are the most common primary brain malignancies.  ...  In recent years, deep convolutional neural networks (CNNs) have achieved great success in the field of computer vision.  ... 
doi:10.3389/fnins.2019.00810 pmid:31474816 pmcid:PMC6707136 fatcat:7nkg73j6p5hvrirvt2pzch2jii
« Previous Showing results 1 — 15 out of 673 results