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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Proceedings ArticleDOI
Densely connected deep U-Net for abdominal multi-organ segmentation
TL;DR: The HU values in CT slices were first windowed in a range to exclude irrelevant organs, and then put into DC U-Net for training, to improve the accuracy of small regin of interest segmentation with limited dataset and a novel loss function.
Journal ArticleDOI
Cardiac MR segmentation based on sequence propagation by deep learning.
TL;DR: Results show that the proposed method for CMR segmentation based on U-Net and combined with image sequence information can segment the myocardial quickly and efficiently and is better than the current state-of-the-art methods.
Journal ArticleDOI
IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound
Dawood Al Chanti,Vanessa Gonzalez Duque,Marion Crouzier,Antoine Nordez,Lilian Lacourpaille,Diana Mateus +5 more
TL;DR: In this paper, a deep Siamese 3D Encoder-Decoder network is deployed to propagate a reference mask annotated by a clinical expert to handle longer changes of the muscle shape over the entire volume.
Journal ArticleDOI
Automatic skull defect restoration and cranial implant generation for cranioplasty.
Jianning Li,Gord von Campe,Antonio Pepe,Christina Gsaxner,Enpeng Wang,Xiaojun Chen,Ulrike Zefferer,Martin Tödtling,Marcell Krall,Hannes Deutschmann,Ute Schäfer,Dieter Schmalstieg,Jan Egger +12 more
TL;DR: Jianningli et al. as discussed by the authors formulated the skull defect restoration as a 3D volumetric shape completion task, where a partial skull volume is completed automatically and the difference between the completed skull and the partial skull is the restored defect; in other words, the implant that can be used in cranioplasty.
Journal ArticleDOI
Beyond the Artificial Intelligence Hype: What Lies Behind the Algorithms and What We Can Achieve.
TL;DR: Although AI-based applications offer great opportunity to improve radiology workflow, several challenges need to be addressed starting from image standardization, sophisticated algorithm development, and large-scale evaluation.
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.
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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.
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.