• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 31 matches

• 2024
• 
  Bindu Puthentharayil Vikraman, A. Jabeena:
  Segmentation based medical image compression of brain magnetic resonance images using optimized convolutional neural network. Multim. Tools Appl. 83(9): 26643-26661 (2024)
• 
  A. Padmanabha Sarma, G. Saranya:
  Segmentation of the Corpus Callosum from Brain Magnetic Resonance Images Using Dual Deep Learning Classifiers and Optimized U-Shaped Neural Networks. SN Comput. Sci. 5(1): 1 (2024)
• 2023
• 
  R. Preetha, M. Jasmine Pemeena Priyadarsini, Nisha J. S.:
  Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images. IEEE Access 11: 138549-138567 (2023)
• 
  B. Sudha Devi, D. S. Misbha:
  Hybrid convolutional neural network based segmentation of visceral and subcutaneous adipose tissue from abdominal magnetic resonance images. J. Ambient Intell. Humaniz. Comput. 14(10): 13333-13347 (2023)
• 
  Retraction Note: Cerebrum Tumor Segmentation of High Resolution Magnetic Resonance Images Using 2D-Convolutional Network with Skull Stripping. Neural Process. Lett. 55(1): 879 (2023)
• 
  Aldir Sousa, Marcelo Toledo, José Eduardo Krieger, Marco Antonio Gutierrez:
  Automatic segmentation of stroke lesions in T1-weighted magnetic resonance images with convolutional neural networks. Medical Imaging: Computer-Aided Diagnosis 2023
• 2022
• 
  Nadia A. Farrag, Sathvik Bhagavan, David Sebben, Poojani Ruwanpura, James A. White, Eranga Ukwatta:
  Automated myocardial segmentation of extra-cellular volume mapping cardiac magnetic resonance images using fully convolutional neural networks. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging 2022
• 2021
• 
  S. Niyas, S. Chethana Vaisali, Iwrin Show, T. G. Chandrika, S. Vinayagamani, Chandrasekharan Kesavadas, Jeny Rajan:
  Segmentation of focal cortical dysplasia lesions from magnetic resonance images using 3D convolutional neural networks. Biomed. Signal Process. Control. 70: 102951 (2021)
• 
  Xinhong Mu, Yi Cui, Rongpeng Bian, Long Long, Daliang Zhang, Huawen Wang, Yidong Shen, Jingjing Wu, Guoyou Zou:
  In-depth learning of automatic segmentation of shoulder joint magnetic resonance images based on convolutional neural networks. Comput. Methods Programs Biomed. 211: 106325 (2021)
• 
  Baris Kanber, Jasper M. Morrow, Uros Klickovic, Stephen J. Wastling, Sachit Shah, Pietro Fratta, Amy R. McDowell, Matt G. Hall, Chris A. Clark, Francesco Muntoni, Mary M. Reilly, Michael G. Hanna, Daniel C. Alexander, Tarek A. Yousry, John S. Thornton:
  Musclesense: a Trained, Artificial Neural Network for the Anatomical Segmentation of Lower Limb Magnetic Resonance Images in Neuromuscular Diseases. Neuroinformatics 19(2): 379-383 (2021)
• 
  Liyan Zhang, Hao Zhou, Juan Wang, Lei Wang, Chengyi Xia:
  Automatic segmentation for meniscus magnetic resonance images of knee joint based on Mask region-based convolution neural network. CSAI 2021: 50-56
• 
  Rajarajeswari Muthusivarajan, Adrian Celaya, Joshua P. Yung, Satish Viswanath, Daniel S. Marcus, Caroline Chung, David Fuentes:
  Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy. CoRR abs/2111.01093 (2021)
• 
  (Withdrawn) Cerebrum Tumor Segmentation of High Resolution Magnetic Resonance Images Using 2D-Convolutional Network with Skull Stripping. Neural Process. Lett. 53(4): 2567-2580 (2021)
• 2019
• 
  Wei-Kai Lee, Chih-Chun Wu, Tzu-Hsuan Huang, Chun-Yi Lin, Cheng-Chia Lee, Wen-Yuh Chung, Po-Shan Wang, Chia-Feng Lu, Hsiu-Mei Wu, Wan-Yuo Guo, Yu-Te Wu:
  Segmentation of Vestibular Schwannoma from Multi-parametric Magnetic Resonance Images using Convolutional Neural Network. DMIP 2019: 8-11
• 
  Fatemeh Zabihollahy, James A. White, Eranga Ukwatta:
  Fully automated segmentation of left ventricular myocardium from 3D late gadolinium enhancement magnetic resonance images using a U-net convolutional neural network-based model. Medical Imaging: Computer-Aided Diagnosis 2019: 109503C
• 
  Stefan Grivalsky, Martin Tamajka, Wanda Benesova:
  Segmentation of gliomas in magnetic resonance images using recurrent neural networks. TSP 2019: 539-542
• 2018
• 
  Avinash Kori, Mehul Soni, B. Pranjal, Mahendra Khened, Alex Varghese, Ganapathy Krishnamurthi:
  Ensemble of Fully Convolutional Neural Network for Brain Tumor Segmentation from Magnetic Resonance Images. BrainLes@MICCAI (2) 2018: 485-496
• 2017
• 
  XuLei Yang, Zeng Zeng, Yi Su:
  Deep convolutional neural networks for automatic segmentation of left ventricle cavity from cardiac magnetic resonance images. IET Comput. Vis. 11(8): 643-649 (2017)
• 2016
• 
  Archontis Giannakidis, Konstantinos Kamnitsas, Veronica Spadotto, Jennifer Keegan, Gillian Smith, Ben Glocker, Daniel Rueckert, Sabine Ernst, Michael A. Gatzoulis, Dudley J. Pennell, Sonya Babu-Narayan, David N. Firmin:
  Fast Fully Automatic Segmentation of the Severely Abnormal Human Right Ventricle from Cardiovascular Magnetic Resonance Images Using a Multi-Scale 3D Convolutional Neural Network. SITIS 2016: 42-46
• 2003
• 
  Andreas Hadjiprocopis, Paul S. Tofts:
  An automatic lesion segmentation method for fast spin echo magnetic resonance images using an ensemble of neural networks. NNSP 2003: 709-718
• 
  Andreas Hadjiprocopis, Paul S. Tofts:
  Towards an Automatic Lesion Segmentation Method for Dual Echo Magnetic Resonance Images Using an Ensemble of Neural Networks. WIRN 2003: 148-157
• 2000
• 
  Ross J. Maxwell, John Wilson, Gillian M. Tozer, Paul R. Barber, Borivoj Vojnovic:
  Segmentation of Magnetic Resonance Images According to Contrast Agent Uptake Kinetics Using a Competitive Neural Network. ANNIMAB 2000: 93-98
• 1997
• 
  Rachid Sammouda, Noboru Niki, Hiromu Nishitani:
  Hopfield neural network for the multichannel segmentation of magnetic resonance cerebral images. Pattern Recognit. 30(6): 921-927 (1997)
• 1996
• 
  Bahram Ashjaei, Hamid Soltanian-Zadeh:
  A comparative analysis of neural network methodologies for segmentation of magnetic resonance images. ICIP (2) 1996: 257-260
• 1995
• 
  Rachid Sammouda, Noboru Niki, Hiromu Nishitani:
  Multichannel segmentation of magnetic resonance cerebral images based on neural networks. ICIP 1995: 484-487
• 
  Rachid Sammouda, Noboru Niki, Hiromu Nishitani:
  Neural Networks for the Segmentation of Magnetic Resonance Images. ICSC 1995: 339-346
• 1994
• 
  Andrew J. Worth, David N. Kennedy:
  Segmentation of magnetic resonance brain images using analogue constraint satisfaction neural networks. Image Vis. Comput. 12(6): 345-354 (1994)
• 1993
• 
  Andrew J. Worth, David N. Kennedy:
  Segmentation of Magnetic Resonance Brain Images using Analog Constraint Satisfaction Neural Networks. DAGM-Symposium 1993: 126-133
• 1992
• 
  Koichi Oshio, Manbir Singh:
  Neural network approach to segmentation of magnetic resonance head images. Int. J. Imaging Syst. Technol. 4(2): 130-134 (1992)
• 
  Sundar C. Amartur, David Piraino, Yoshiyasu Takefuji:
  Optimization neural networks for the segmentation of magnetic resonance images. IEEE Trans. Medical Imaging 11(2): 215-220 (1992)

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