Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

Kleesiek, Jens, Urban, Gregor, Hubert, Alexander, Schwarz, Daniel, Maier-Hein, Klaus, Bendszus, Martin, Biller, Armin·2016·NeuroImage

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  18. A comparative analysis and classification of cancerous brain tumors detection based on classical machine learning and deep transfer learning models
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  19. On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging
    Nima Tajbakhsh, Jae Y. Shin, Suryakanth Gurudu, R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway, Jianming Liang · 2017 · Advances in computer vision and pattern recognition
  20. Orchestral fully convolutional networks for small lesion segmentation in brain MRI
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  23. An Imbalanced R-STDP Learning Rule in Spiking Neural Networks for Medical Image Classification
    Qian Zhou, Cong Ren, Saibing Qi · 2020 · IEEE Access
  24. Artificial neural networks as clinical decision support systems
    Imran Shafi, Sana Ansari, Sadia Din, Gwanggil Jeon, Anand Paul · 2021 · Concurrency and Computation Practice and Experience
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    G. Devika, Asha Gowda Karegowda · 2020 · Advances in computational intelligence and robotics book series
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    Ibtisam Aslam, Fariha Aamir, Miklós Kassai, Lindsey Alexandra Crowe, Pierre‐Alexandre Poletti, Sophie de Seigneux, Solange Moll, Lena Berchtold, Jean‐Paul Vallée · 2023 · PLoS ONE
  29. Neuralizer: General Neuroimage Analysis without Re-Training
    Steffen Czolbe, Adrian Vasile Dalca · 2023 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
  30. Alzheimer’s disease diagnosis by 3D-SEConvNeXt
    Zhongyi Hu, Yuhang Wang, Lei Xiao · 2025 · Journal Of Big Data
  31. A Comprehensive Review of Brain Tumour Detection Mechanisms
    Praveen Kumar Ramtekkar, Anjana Pandey, Mahesh Kumar Pawar · 2023 · The Computer Journal
  32. A review of deep learning models for medical diagnosis
    Seshadri Sastry Kunapuli, Praveen Chakravarthy Bhallamudi · 2021 · Elsevier eBooks
  33. A skull stripping from brain MRI using adaptive iterative thresholding and mathematical morphology
    Mouli Laha, Prasun Chandra Tripathi, Soumen Bag · 2018 · International Conference on Recent Advances in Information Technology
  34. Quality-driven deep active learning method for 3D brain MRI segmentation
    Zhenxi Zhang, Jie Li, Chun-Na Tian, Zhu-Si Zhong, Z. Jiao, Xin-Bo Gao · 2021 · Neurocomputing
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    Pae Sun Suh, Wooseok Jung, Chong Hyun Suh, Jinyoung Kim, Jio Oh, Hwon Heo, Woo Hyun Shim, Jae‐Sung Lim, Jae‐Hong Lee, Ho Sung Kim et al. · 2023 · Frontiers in Neurology
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    Peng Ai, D. Wang, J. F. Hu, Xiaoying Sun · 2018 · Journal of Instrumentation
  37. Brain tumor segmentation on Multimodal MRI scans using EMAP Algorithm
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    Qi Zhang, Jiaolun Geng, Ming Zhang, Tianyou Kan, Liao Wang, Songtao Ai, Hongjiang Wei, Lichi Zhang, Chenglei Liu · 2023 · Quantitative Imaging in Medicine and Surgery
  42. k-strip: A novel segmentation algorithm in k-space for the application of skull stripping
    Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala, Johannes Haubold, Felix Nensa, Kevin Alexander Kroeninger, Jan Egger, Jens Kleesiek · 2023 · Computer Methods and Programs in Biomedicine
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    Murat Berat UÇAR, Kemal Akyol, Ümit Atila, Emine Uçar · 2021 · IRBM
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    Christian McDaniel, Shannon P. Quinn · 2019 · Proceedings of the Python in Science Conferences
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    Minh-Trieu Tran, Soo-Hyung Kim, Hyung-Jeong Yang, Guee-Sang Lee, In‐Jae Oh, Sae‐Ryung Kang · 2021 · Sensors
  46. A Systematic Approach for Deep Learning Based Brain Tumor Segmentation
    Roohi Sille, Tanupriya Choudhury, Piyush Chauhan, Durgansh Sharma · 2021 · Ingénierie des systèmes d information
  47. Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps
    Umaseh Sivanesan, Luis Henrique Braga, Ranil Sonnadara, Kiret Dhindsa · 2019 · arXiv (Cornell University)
  48. Exploring sequence transformation in magnetic resonance imaging via deep learning using data from a single asymptomatic patient
    James A. Grant‐Jacob, Chris Everitt, Robert W. Eason, Leonard J King, Ben Mills · 2021 · Journal of Physics Communications
  49. Automated segmentation of multiple sclerosis lesions based on convolutional neural networks
    Nada Haj Messaoud, Asma Mansour, Mouna Aissi, Rim Ayari, Mahbouba Ayed Frih, Asma Ben Abdallah, Mohamed Hédi Bedoui · 2022 · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization
  50. Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks
    Saeed Moazami, Deep Ray, Daniel Pelletier, Assad A. Oberai · 2023 · IEEE Transactions on Medical Imaging
  51. A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases
    Li-Mei Wang, Yue Sun, J. Seidlitz, R. Bethlehem, A. Alexander-Bloch, L. Dorfschmidt, Gang Li, J. Elison, Wei-Li Lin, Li Wang · 2025 · Nature Biomedical Engineering
  52. An accurate and robust skull stripping method for 3-D magnetic resonance brain images
    Shaswati Roy, Pradipta Maji · 2018 · Magnetic Resonance Imaging
  53. Brain Tissue Segmentation via Deep Convolutional Neural Networks
    Thulasi Bikku, Jayavarapu Karthik, Ganga Rama Koteswara Rao, K P N V Satya Sree, P V V S Srinivas, Chitturi Prasad · 2021 · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
  54. Ear Biometrics Using Deep Learning: A Survey
    Aimee Booysens, Serestina Viriri · 2022 · Applied Computational Intelligence and Soft Computing
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    Weiya Wang, Haifeng Zhou, Yu‐Xin Yan, Xiao Cheng, Peng Yang, Liangzhi Gan, Shaolong Kuang · 2022 · International Journal of Medical Robotics and Computer Assisted Surgery
  56. Automatic analysis of skull thickness, scalp-to-cortex distance and association with age and sex in cognitively normal elderly
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    G. Dheepak, J. Anita Christaline, D. Vaishali · 2023 · IET Image Processing
  58. Multi-atlas based neonatal brain extraction using a two-level patch-based label fusion strategy
    Negar Noorizadeh, Kamran Kazemi, Habibollah Danyali, Ardalan Aarabi · 2019 · Biomedical Signal Processing and Control
  59. An adaptive probabilistic atlas for anomalous brain segmentation in MR images
    Samuel Botter Martins, Jordão Bragantini, Alexandre X. Falcão, Clarissa Lin Yasuda · 2019 · Medical Physics
  60. Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective
    G. Erus, M. Habes, C. Davatzikos · 2020 · Elsevier eBooks
  61. Histogram Equalization based Enhancement and MR Brain Image Skull Stripping using Mathematical Morphology
    Zahid Ullah, Su-Hyun Lee, Donghyeok An · 2020 · International Journal of Advanced Computer Science and Applications
  62. Assessing the Accuracy and Reproducibility of PARIETAL : A Deep Learning Brain Extraction Algorithm
    Sergi Valverde, Llucia Coll, Liliana Valencia, Albert Clèrigues, Arnau Oliver, Joan Carles Vilanova, Lluís Ramió‐Torrentà, Àlex Rovira, Xavier Lladó · 2021 · Journal of Magnetic Resonance Imaging
  63. A Multi-Category Brain Tumor Classification Method Bases on Improved ResNet50
    Linguo Li, Shujing Li, Jian Su · 2021 · Computers, materials & continua/Computers, materials & continua (Print)
  64. A Bibliometric Review: Brain Tumor Magnetic Resonance Imagings Using Different Convolutional Neural Network Architectures
    Arati Rath, Dipak Kumar Mohanty, Bhabani Shankar Prasad Mishra, Dilip Kumar Bagal · 2022 · World Neurosurgery
  65. Asymmetric Similarity Loss Function to Balance Precision and Recall in Highly Unbalanced Deep Medical Image Segmentation
    Seyed Raein Hashemi, Seyed Sadegh Mohseni Salehi, Deniz Erdogmus B, Sanjay P. Prabhu, Simon Keith Warfield, Ali Gholipour · 2018 · arXiv (Cornell University)
  66. Medical lesion segmentation by combining multimodal images with modality weighted UNet
    Xiner Zhu, Yichao Wu, Haoji Roland Hu, Xianwei Zhuang, Jincao Yao, Di Ou, Wei Li, Mei Ling Song, Na Feng, Dong Ming Xu · 2022 · Medical Physics
  67. A Modified U-Net Based Framework for Automated Segmentation of Hippocampus Region in Brain MRI
    Nosheen Sohail, Syed Muhammad Anwar · 2022 · IEEE Access
  68. Deep learning network for integrated coil inhomogeneity correction and brain extraction of mixed MRI data
    Kai‐Hsiang Chuang, Pei-Huan Wu, Zengmin Li, Kang‐Hsing Fan, Jun‐Cheng Weng · 2022 · Scientific Reports
  69. Design for Artificial Intelligence: Proposing a Conceptual Framework Grounded in Data Wrangling
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  70. Multi-scale V-net architecture with deep feature CRF layers for brain extraction
    Jong Sung Park, Shreyas Fadnavis, Eleftherios Garyfallidis · 2024 · Communications Medicine
  71. Brain Region Segmentation using Convolutional Neural Network
    D. Selvathi, T. Vanmathi · 2018 · International Conference on Electrical Energy Systems (ICEES)
  72. Double U-Nets for Image Segmentation by Integrating the Region and Boundary Information
    Wei Guo, Hanxun Zhou, Zhaoxuan Gong, Guodong Zhang · 2021 · IEEE Access
  73. Att2ResNet : A deep attention‐based approach for melanoma skin cancer classification
    Said Yacine Boulahia, Mohamed Akrem Benatia, Abderrahmane Bouzar · 2021 · International Journal of Imaging Systems and Technology
  74. A Fast 3D Brain Extraction and Visualization Framework Using Active Contour and Modern OpenGL Pipelines
    Junchen Wang, Zhen Sun, Honglei Ji, Xiaohui Zhang, Tianmiao Wang, Yu Shen · 2019 · IEEE Access
  75. Application of Deep Learning in Healthcare: A Survey on Brain Tumor Detection
    Khadija Elaissaoui, Mohammed Ridouani · 2023 · ITM Web of Conferences
  76. Skull Stripping Using Confidence Segmentation Convolution Neural Network
    Kaiyuan Chen, Jingyue Shen, Fabien Scalzo · 2018 · Lecture notes in computer science
  77. A Macaque Brain Extraction Model Based on U-Net Combined with Residual Structure
    Qianshan Wang, Hong Fei, Saddam Naji Abdu Nasher, Xiaoluan Xia, Haifang Li · 2022 · Brain Sciences
  78. Accurate brain extraction using Active Shape Model and Convolutional Neural Networks.
    Nguyen Ho Minh Duy, Manh-Duy Nguyen, Mai Thanh Nhat Truong, Thanh Binh Nguyen · 2018 · arXiv (Cornell University)
  79. A multimodal 2D Convolutional Neural Network for Multiple Sclerosis Lesion Detection
    Florian Raab, Simon Wein, Mark W. Greenlee, Wilhelm M. Malloni, Elmar Wolfgang Lang · 2022
  80. An Overview of Neural Network Architectures for Healthcare
    Idongesit Zion, Simeon Ozuomba, Philip Michael Asuquo · 2020 · International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS)
  81. Automated Skull Stripping in Mouse Functional Magnetic Resonance Imaging Analysis Using 3D U-Net
    Guohui Ruan, Jiaming Liu, Ziqi An, Kaiibin Wu, Chuanjun Tong, Qiang Liu, Ping Liang, Zhifeng Liang, Wufan Chen, Xinyuan Zhang et al. · 2022 · Frontiers in Neuroscience
  82. IterNet++: An improved model for retinal image segmentation by curvelet enhancing, guided filtering, offline hard‐sample mining, and test‐time augmenting
    Mingyu Zhu, Kun Zeng, Gigin Lin, Yongyi Gong, Tianyong Hao, Kanoksak Wattanachote, Xiaonan Luo · 2022 · IET Image Processing
  83. HDFU-Net: An Improved Version of U-Net using a Hybrid Dice Focal Loss Function for Multi-modal Brain Tumor Image Segmentation
    Islem Gammoudi, Raja Ghozi, Mohamed Ali Mahjoub · 2022 · International Conference on Cyberworlds (CW)
  84. Comparative Analysis of Deep Learning Models on Brain Tumor Segmentation Datasets: BraTS 2015-2020 Datasets
    Mukul Aggarwal, Amod Kumar Tiwari, M. Partha Sarathi · 2022 · Revue d intelligence artificielle
  85. Recent advancement in learning methodology for segmenting brain tumor from magnetic resonance imaging -a review
    Sunayana G. Domadia, Falgun N. Thakkar, Mayank A. Ardeshana · 2023 · Multimedia Tools and Applications
  86. Automated, fast, robust brain extraction on contrast-enhanced T1-weighted MRI in presence of brain tumors: an optimized model based on multi-center datasets
    Yuen Teng, Chaoyue Chen, Xin Shu, Fumin Zhao, Lei Zhang, Jianguo Xu · 2023 · European Radiology
  87. Backdoor Attack on 3D Grey Image Segmentation
    Honghui Xu, Zhipeng Cai, Zuobin Xiong, Wei Li · 2023 · Proceedings
  88. Deep learning-based classification of multi-categorical Alzheimer's disease data.
    David S Cohen, Kristy A. Carpenter, Juliet T. Jarrell, Xudong Huang · 2019 · PubMed
  89. Glioma segmentation of optimized 3D U-net and prediction of multi-modal survival time
    Qihong Liu, Kai Liu, Antonio Bolufé-Röhler, Jing Cai, Ling He · 2021 · Neural Computing and Applications
  90. Improved Deep Convolutional Neural Networks via Boosting for Predicting the Quality of In Vitro Bovine Embryos
    Turki Turki, Zhi Wei · 2022 · Electronics
  91. GUBS: Graph-Based Unsupervised Brain Segmentation in MRI Images
    Simeon Mayala, Ida Herdlevær, Jonas Bull Haugsøen, Shamundeeswari Anandan, Nello Blaser, Sonia Gavasso, Morten Brun · 2022 · Journal of Imaging
  92. Segmenting localized corrosion from rust-removed metallic surface with deep learning algorithm
    Shouxin Zhang, Zili Li, Chao Yang, Chengyuan Zhu · 2019 · Journal of Electronic Imaging
  93. Ensembling Transformer-Based Models for 3D Ischemic Stroke Segmentation in Non-Contrast CT
    Lyailya Cherikbayeva, Vladimir Berikov, Зарина Меліс, Arman Yeleussinov, Dametken Baigozhanova, Nurbolat Tasbolatuly, Temirbekova Zhanerke Erlanovna, Denis Mikhailapov · 2025 · Applied Sciences
  94. RS 2 -Net: An end-to-end deep learning framework for rodent skull stripping in multi-center brain MRI
    Yongkang Lin, Yanhui Ding, Shulei Chang, Xinting Ge, Xiaodan Sui, Yanyun Jiang · 2024 · NeuroImage
  95. Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features
    Tomoki Sasagasako, Akihiko Ueda, Yohei Mineharu, Yusuke Mochizuki, Souichiro Doi, Silsu Park, Yukinori Terada, Noritaka Sano, Masahiro Tanji, Yoshiki Arakawa et al. · 2024 · PLoS ONE
  96. Auto-contouring for Image-Guidance and Treatment Planning
    Rachel B. Ger, Tucker Netherton, Dong Joo Rhee, Laurence Edward Court, Jinzhong Yang, Carlos E. Cardenas · 2022 · Springer eBooks
  97. A Survey on Convolutional Neural Networks for MRI Analysis
    Shreya Hardaha, Damodar Reddy Edla, Saidi Reddy Parne · 2022 · Wireless Personal Communications
  98. Artifacts and Skull Stripping: An Application Towards the Preprocessing for Brain Abnormalities Detection from MRI
    Sudipta Singha Roy, Debnath Bhattacharyya, Samir Kumar Bandyopadhyay, Tai-hoon Kim · 2017 · International Journal of Control and Automation
  99. Advanced deep learning methods for biomedical information analysis: An editorial
    Yudong Zhang, Francesco Carlo Morabito, Dinggang G. Shen, Khan Muhammad · 2020 · Neural Networks
  100. Automatic Parotid Gland Segmentation in MVCT Using Deep Convolutional Neural Networks
    Junqian Zhang, Yingming Sun, Hongen Liao, Jian Zhu, Yuan Zhang · 2021 · ACM Transactions on Computing for Healthcare