HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation
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Jing Yuan | Jose Dolz | Christian Desrosiers | Herve Lombaert | Karthik Gopinath | Ismail Ben Ayed | Jing Yuan | H. Lombaert | J. Dolz | Christian Desrosiers | Karthik Gopinath | I. ben Ayed | Ismail ben Ayed
[1] Alexander Leemans,et al. Disruption of the Cerebral White Matter Network Is Related to Slowing of Information Processing Speed in Patients With Type 2 Diabetes , 2013, Diabetes.
[2] Hao Chen,et al. VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images , 2017, NeuroImage.
[3] Xinjian Chen,et al. Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images , 2015, IEEE Transactions on Image Processing.
[4] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[5] Yong Yin,et al. Lung Tumor Delineation Based on Novel Tumor-Background Likelihood Models in PET-CT Images , 2014, IEEE Transactions on Nuclear Science.
[6] Örjan Smedby,et al. Automatic brain segmentation using artificial neural networks with shape context , 2018, Pattern Recognit. Lett..
[7] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Yu Long,et al. Regularization of convolutional neural networks using ShuffleNode , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).
[9] Yaozong Gao,et al. Fully convolutional networks for multi-modality isointense infant brain image segmentation , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).
[10] Jayaram K. Udupa,et al. Co-segmentation of Functional and Anatomical Images , 2012, MICCAI.
[11] Petronella Anbeek,et al. Probabilistic Brain Tissue Segmentation in Neonatal Magnetic Resonance Imaging , 2008, Pediatric Research.
[12] Heiko Schöder,et al. Hybrid imaging (SPECT/CT and PET/CT): improving therapeutic decisions. , 2009, Seminars in nuclear medicine.
[13] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[14] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[15] Max A. Viergever,et al. Automatic Segmentation of MR Brain Images With a Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.
[16] Shuiwang Ji,et al. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation , 2015, NeuroImage.
[17] Junjie Bai,et al. Optimal Co-Segmentation of Tumor in PET-CT Images With Context Information , 2013, IEEE Transactions on Medical Imaging.
[18] Hao Chen,et al. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Liver Tumor Segmentation from CT Volumes , 2017, ArXiv.
[19] Brian B. Avants,et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.
[20] Terry M. Peters,et al. Segmentation of thalamic nuclei using a modified k-means clustering algorithm and high-resolution quantitative magnetic resonance imaging at 1.5 T , 2007, NeuroImage.
[21] Sébastien Ourselin,et al. AdaPT: An adaptive preterm segmentation algorithm for neonatal brain MRI , 2013, NeuroImage.
[22] Olivier Commowick,et al. MSSEG Challenge Proceedings: Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure , 2016, MICCAI 2016.
[23] Jingdong Wang,et al. Interleaved Group Convolutions , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[24] J. Buatti,et al. Globally Optimal Tumor Segmentation in PET-CT Images: A Graph-Based Co-segmentation Method , 2011, IPMI.
[25] Dimos Baltas,et al. Esophagus segmentation in CT via 3D fully convolutional neural network and random walk , 2017, Medical physics.
[26] Gang Li,et al. Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge , 2019, IEEE Transactions on Medical Imaging.
[27] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Nikos Komodakis,et al. Wide Residual Networks , 2016, BMVC.
[29] Andrea Bergmann,et al. Statistical Parametric Mapping The Analysis Of Functional Brain Images , 2016 .
[30] Ben Glocker,et al. DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images , 2017, ArXiv.
[31] Sasa Mutic,et al. Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning. , 2007, Medical physics.
[32] Sébastien Ourselin,et al. Scalable multimodal convolutional networks for brain tumour segmentation , 2017, MICCAI.
[33] Huan Yu,et al. Coregistered FDG PET/CT-Based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning , 2009, IEEE Transactions on Medical Imaging.
[34] Chi-Wing Fu,et al. H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes , 2018, IEEE Transactions on Medical Imaging.
[35] Alex Rovira,et al. Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches , 2012, Inf. Sci..
[36] Xinjian Chen,et al. Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images , 2013, Medical Image Anal..
[37] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[38] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[39] John H. Gilmore,et al. Automatic segmentation of MR images of the developing newborn brain , 2005, Medical Image Anal..
[40] Zhuowen Tu,et al. Deeply-Supervised Nets , 2014, AISTATS.
[41] Chenliang Xu,et al. MRI tumor segmentation with densely connected 3D CNN , 2018, Medical Imaging.
[42] Simon K. Warfield,et al. Automatic segmentation of newborn brain MRI , 2009, NeuroImage.
[43] Daniel Rueckert,et al. A review on automatic fetal and neonatal brain MRI segmentation , 2017, NeuroImage.
[44] Alex Rovira,et al. Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach , 2017, NeuroImage.
[45] David Dagan Feng,et al. Primary lung tumor segmentation from PET–CT volumes with spatial–topological constraint , 2015, International Journal of Computer Assisted Radiology and Surgery.
[46] M. Benders,et al. Automatic segmentation of neonatal brain MRI using atlas based segmentation and machine learning approach , 2012 .
[47] Max A. Viergever,et al. elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.
[48] Jose Dolz,et al. 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study , 2016, NeuroImage.
[49] Mohammad Havaei,et al. HeMIS: Hetero-Modal Image Segmentation , 2016, MICCAI.
[50] Jürgen Schmidhuber,et al. Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation , 2015, NIPS.
[51] Y. Ung,et al. Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT , 2013, International journal of molecular imaging.
[52] Konstantinos Kamnitsas,et al. Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI , 2015 .
[53] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[54] Kilian Q. Weinberger,et al. Multi-Scale Dense Convolutional Networks for Efficient Prediction , 2017, ArXiv.
[55] Yaozong Gao,et al. Segmentation of neonatal brain MR images using patch-driven level sets , 2014, NeuroImage.
[56] Dinggang Shen,et al. Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation , 2017, Comput. Medical Imaging Graph..
[57] Rama Chellappa,et al. HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[58] Gregory Shakhnarovich,et al. FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.
[59] Nitish Srivastava,et al. Multimodal learning with deep Boltzmann machines , 2012, J. Mach. Learn. Res..
[60] Amir Alansary,et al. MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans , 2015, Comput. Intell. Neurosci..
[61] Konstantinos Kamnitsas,et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks , 2016, IPMI.
[62] Yaozong Gao,et al. Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation , 2014, NeuroImage.
[63] Kilian Q. Weinberger,et al. Deep Networks with Stochastic Depth , 2016, ECCV.
[64] Hao Chen,et al. Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets , 2017, MICCAI.
[65] Dinggang Shen,et al. Automatic segmentation of neonatal images using convex optimization and coupled level sets , 2011, NeuroImage.
[66] Yoshua Bengio,et al. Understanding intermediate layers using linear classifier probes , 2016, ICLR.
[67] Jing Yuan,et al. Isointense infant brain segmentation with a hyper-dense connected convolutional neural network , 2017, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[68] Simon K. Warfield,et al. Segmentation of newborn brain MRI , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..
[69] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[70] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.