Journal ArticleDOI
3D deeply supervised network for automated segmentation of volumetric medical images.
TLDR
The proposed 3D DSN is capable of conducting volume‐to‐volume learning and inference, which can eliminate redundant computations and alleviate the risk of over‐fitting on limited training data, and the3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model.About:
This article is published in Medical Image Analysis.The article was published on 2017-10-01. It has received 507 citations till now. The article focuses on the topics: Scale-space segmentation & Image segmentation.read more
Citations
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Journal ArticleDOI
Deep learning network with Euclidean similarity factor for Brain MR Tumor segmentation and volume estimation
G. Anand Kumar,P. V. Sridevi +1 more
TL;DR: The major goal of this paper is to isolate tumor region from nontumor regions and the estimation of tumor volume and the analysis of segmentation for accurate segmentation.
Posted Content
Multi-stream 3D FCN with Multi-scale Deep Supervision for Multi-modality Isointense Infant Brain MR Image Segmentation
Guodong Zeng,Guoyan Zheng +1 more
TL;DR: In this article, a context-guided, multi-stream fully convolutional networks (FCN) was proposed to segment 6-month infant brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF).
Journal ArticleDOI
Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning.
Yinglin Peng,Yimei Liu,Guanzhu Shen,Zijie Chen,Meining Chen,Jingjing Miao,Chong Zhao,Jincheng Deng,Zhen Yu Qi,Xiaowu Deng +9 more
TL;DR: In this paper , a modified encoder-decoder architecture based fully convolutional neural network, OrganNet, was used for simultaneous auto-segmentation of 24 organs at risk in the head and neck, followed by validation tests and evaluation of clinical application.
Journal ArticleDOI
Harmonizing Pathological and Normal Pixels for Pseudo-Healthy Synthesis
Yunlong Zhang,Xin Lin,Yihong Zhuang,LiyanSun,Yue Huang,Xinghao Ding,Guisheng Wang,Li Yang,Yizhou Yu +8 more
TL;DR: A new type of discriminator is presented, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images, and a reliable metric is proposed by utilizing two attributes of label noise to measure the health of synthetic images.
Journal ArticleDOI
MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation
TL;DR: In this article , a multi-scale self-ensembling based unsupervised domain adaptation (UDA) framework was proposed for automatic segmentation of two key brain structures (Vestibular Schwannoma (VS) and Cochlea on high-resolution T2 images.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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