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
Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
Inês Domingues,Gisèle Pereira,Pedro Martins,Hugo Duarte,João A. M. Santos,Pedro Henriques Abreu +5 more
TL;DR: A survey of the use of DL architectures in computer-assisted imaging contexts, attending two different image modalities: the actively studied computed tomography and the under-studied positron emission tomography, as well as the combination of both modalities, which has been an important landmark in several decisions related to numerous diseases.
Journal ArticleDOI
Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases
Sarah Lindgren Belal,May Sadik,Reza Kaboteh,Olof Enqvist,Johannes Ulén,Mads Hvid Poulsen,Jane Angel Simonsen,Poul Flemming Høilund-Carlsen,Lars Edenbrandt,Elin Trägårdh +9 more
TL;DR: The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.
Journal ArticleDOI
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
TL;DR: In this paper, a CNN-based model was proposed to combine the advantages of the short-range 3D context and the long-range 2D context for tumor segmentation in multisequence MR images.
Journal ArticleDOI
Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation
Eleftherios Trivizakis,Georgios C. Manikis,Katerina Nikiforaki,Konstantinos Drevelegas,Manos Constantinides,Antonios Drevelegas,Kostas Marias +6 more
TL;DR: It is suggested that the proposed 3-D CNN architecture can bring significant benefit in DW-MRI liver discrimination and potentially, in numerous other tissue classification problems based on tomographic data, especially in size-limited, disease-specific clinical datasets.
Journal ArticleDOI
Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation.
TL;DR: The proposed SBBS-CNN provides an accurate and effective tool for automated liver segmentation and showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms.
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
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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.