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
More filters
Book ChapterDOI
Deep Volumetric Shape Learning for Semantic Segmentation of the Hip Joint from 3D MR Images
Guodong Zeng,Guoyan Zheng +1 more
TL;DR: This paper addresses the problem of segmentation of the hip joint including both the acetabulum and the proximal femur in three-dimensional magnetic resonance images by proposing a fully convolutional volumetric auto encoder that learns a volumetry representation from manual segmentation in order to regularize the segmentation results obtained from a fully Convolutional network.
Journal ArticleDOI
Left ventricle quantification with sample-level confidence estimation via Bayesian neural network
Wufeng Xue,Guo Tingting,Dong Ni +2 more
TL;DR: Experiments validate that the method introduced by introducing the uncertainty analysis theory into the LV quantification network not only improved the quantification performance with an uncertainty-weighted regression loss but also is capable of providing for each sample the confidence level of the estimation results for clinicians' further consideration.
Journal ArticleDOI
A Survey on Shape-Constraint Deep Learning for Medical Image Segmentation
TL;DR: A broad overview of recent literature on bringing explicit anatomical constraints for medical image segmentation is given, the shortcomings and opportunities are discussed and the potential shift towards implicit shape modelling is elaborated as discussed by the authors .
Posted Content
BiLingUNet: Image Segmentation by Modulating Top-Down and Bottom-Up Visual Processing with Referring Expressions
TL;DR: It is found that using language to modulate both bottom-up and top-down visual processing works better than just making the top- down processing language-conditional.
Journal ArticleDOI
A novel solution of using deep learning for prostate cancer segmentation: enhanced batch normalization
TL;DR: This research aims for accurate segmentation of prostate on MR images by combining multi-level features for decreasing the processing time of the process in prostate surgery and improving the segmentation accuracy and performance.
References
More filters
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.