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
Posted Content
A survey on shape-constraint deep learning for medical image segmentation.
TL;DR: A broad overview of recent literature on bringing anatomical constraints for medical image segmentation is given, the shortcomings and opportunities of the proposed methods are thoroughly discussed and potential future work is elaborated as discussed by the authors.
Journal ArticleDOI
Bridge Segmentation Performance Gap Via Evolving Shape Prior
Chaoyu Chen,Xin Yang,Haoran Dou,Ruobing Huang,Xiaoqiong Huang,Xu Wang,Chong Duan,Shengli Li,Wufeng Xue,Pheng-Ann Heng,Dong Ni +10 more
TL;DR: The feasibility of case adaptation as a general strategy in enhancing the robustness of deep segmentation networks is investigated, with comprehensive analyses proving its efficacy and efficiency.
Journal ArticleDOI
Deep and statistical learning in biomedical imaging: State of the art in 3D MRI brain tumor segmentation
TL;DR: In this paper , a review of major statistical, deep learning, and probabilistic deep learning models and their applications in brain imaging research with a focus on MRI-based brain tumor segmentation is presented.
Proceedings ArticleDOI
Improving Semantic Segmentation of 3D Medical Images on 3D Convolutional Neural Networks
TL;DR: This work aims to improve the task of binary semantic segmentation of volumetric medical images acquired by Magnetic Resonance Imaging (MRI) using a preexisting Three-Dimensional Convolutional Neural Network (3D CNN) architecture by proposing a formulation of a loss function based on a similarity coefficient, used for measuring the spatial overlap between the prediction and ground truth.
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.