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
Automatic liver and tumour segmentation from CT images using Deep learning algorithm
TL;DR: In this article , an automatic method based on semantic segmentation convolutional neural networks (CNNs) was proposed to segment liver from CT scans and lesions from segmented liver part, which achieved Dice Similarity Coefficients (DSCs) of 96.35% and 89.28% and accuracy of 99.71% and 99.72% for liver and tumour segmentation, respectively.
Book ChapterDOI
DSMS-FCN: A Deeply Supervised Multi-scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images
Guodong Zeng,Guoyan Zheng +1 more
TL;DR: A deeply supervised multi-scale fully convolutional network is proposed for segmentation of IVDs in 3D MR images that can directly map a whole volumetric data to its volume-wise labels after training.
Journal ArticleDOI
X-CTRSNet: 3D cervical vertebra CT reconstruction and segmentation directly from 2D X-ray images
TL;DR: X-CTRSNet as mentioned in this paper combines the reciprocally coupled SpaDRNet for reconstruction and MulSISNet for segmentation, and a RSC Learning for tasks consistency, and it successfully reconstructs and segments the 3D C-vertebra CT from the 2D X-ray images with a PSNR of 24.58 dB, an SSIM of 0.749, and an average Dice of 80.44%.
Journal ArticleDOI
BE-FNet: 3D Bounding Box Estimation Feature Pyramid Network for Accurate and Efficient Maxillary Sinus Segmentation
TL;DR: A deep neural network with an end-to-end manner to generalize a fully automatic 3D segmentation and an overestimation strategy is presented to avoid overfitting phenomena in conventional multitask networks to address problems of blurring boundary and class imbalance in medical images.
Journal ArticleDOI
Volumetric Severity Assessment of Ebstein Anomaly Using Three-Dimensional Cardiac CT: A Feasibility Study
TL;DR: The CVIA Volumetric Severity Assessment of Ebstein Anomaly Using Three-Dimensional Cardiac CT: A Feasibility Study shows that cardiac CT is increasingly used to evaluate congenital heart disease patients with Ebstein anomaly.
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.