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
Measurement of Glomerular Filtration Rate using Quantitative SPECT/CT and Deep-learning-based Kidney Segmentation.
Junyoung Park,Sungwoo Bae,Sungwoo Bae,Seongho Seo,Sohyun Park,Ji In Bang,Jeong Hee Han,Won Woo Lee,Won Woo Lee,Won Woo Lee,Jae Sung Lee,Jae Sung Lee +11 more
TL;DR: A fully automated GFR quantification method based on a deep learning approach to the 3D segmentation of kidney parenchyma in CT that enables fast and accurate GFR measurement is developed.
Journal ArticleDOI
Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency
Xiangde Luo,Guotai Wang,Wenjun Liao,Jieneng Chen,Tao Song,Yinan Chen,Shichuan Zhang,Dimitris N. Metaxas,Shaoting Zhang +8 more
TL;DR: Wang et al. as mentioned in this paper presented a simple yet efficient consistency regularization approach for semi-supervised medical image segmentation, called Uncertainty Rectified Pyramid Consistency (URPC).
Journal ArticleDOI
Inter-slice Context Residual Learning for 3D Medical Image Segmentation.
TL;DR: Wang et al. as discussed by the authors proposed a 3D context residual network (ConResNet) for 3D medical image segmentation, which consists of an encoder, a segmentation decoder, and a context residual decoder.
Book ChapterDOI
Generalizing Deep Models for Ultrasound Image Segmentation
TL;DR: This paper proposes a self-play training strategy to effectively pre-train all the adversarial modules in their framework to capture the appearance and structure distributions of source corpus to stabilize the online adversarial appearance conversion.
Proceedings ArticleDOI
End-to-End Supervised Lung Lobe Segmentation
TL;DR: Both quantitative and qualitative results show that the proposed method can learn to produce correct lobe segmentations even when trained on a reduced dataset.
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.