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
Neural Network Vessel Lumen Regression for Automated Lumen Cross-Section Segmentation in Cardiovascular Image-Based Modeling
TL;DR: By training machine learning models to directly predict vessel lumen surface points from computed tomography and magnetic resonance medical image data, this work substantially reduces the manual segmentation effort required to build accurate cardiovascular models, and reduces the overall time required to perform patient-specific cardiovascular simulations.
Posted Content
An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images
Shuai Chen,Marleen de Bruijne +1 more
TL;DR: This paper proposes a new end-to-end training method called Posterior-CRF, which applies 3D, fully connected CRF to the posterior probabilities from a CNN and optimizes both CNN and CRF together.
Journal ArticleDOI
Fully automated extraction of the fringe skeletons in dynamic electronic speckle pattern interferometry using a U-Net convolutional neural network
TL;DR: An intelligent method to achieve fully automated extraction of the fringe skeletons in electronic speckle pattern interferometry (ESPI) based on U-Net convolutional neural network is proposed, especially suitable for multiframe fringe patterns processing.
Journal ArticleDOI
Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples
Ju Zhongjian,Qingnan Wu,Wei Yang,Shanshan Gu,Wen Guo,Wang Jinyuan,Ruigang Ge,Hong Quan,Jie Liu,Baolin Qu +9 more
TL;DR: The Dense V-Network algorithm can be used to automatically segment pelvic OARs accurately and efficiently, while shortening patients’ waiting time and accelerating radiotherapy workflow.
Proceedings ArticleDOI
Convolutional-neural-network-based feature extraction for liver segmentation from CT images
TL;DR: A novel Convolutional neural network for liver segmentation (CNN-LivSeg) algorithm that involves three convolutional layers followed by max-pooling layer and two fully connected layers with a final 2- way softmax is used for liver discrimination.
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
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.