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
Automated segmentation of left ventricular myocardium using cascading convolutional neural networks based on echocardiography
TL;DR: A novel multi-task cascaded convolutional neural network (called MTC-Net) to segment the LV myocardium from echocardiography is proposed and achieves state-of-the-art performance on the test set.
Book ChapterDOI
Nonnegative Matrix Factorization Methods for Brain Tumor Segmentation in Magnetic Resonance Images
Harinder Kaur,Ram Sarup Singh +1 more
TL;DR: This paper represents the successful extraction of the whole tumor along with narcotic and edema from brain MR, T2, and FLAIR images and follows the novel framework of nonnegative matrix factorization (NNMF) along with fuzzy clustering and region growing.
Journal ArticleDOI
A novel shape-based loss function for machine learning-based seminal organ segmentation in medical imaging
TL;DR: The quantitative evaluation of the segmentation results indicated that the proposed PCA-based loss function would lead to significant improvements in organ/structure segmentations from medical images, including improvements in hippocampus and heart segmentation from MR and CT images.
Posted ContentDOI
Fully Automatic initialization and segmentation of left and right ventricles for large-scale cardiac MRI using a deeply supervised network and 3D-ASM.
TL;DR: In this paper , a hybrid and robust algorithm is proposed to improve the accuracy of automatic left and right ventricle segmentation by harnessing the localization accuracy of deep learning and the morphological accuracy of 3D-ASM (threedimensional active shape models).
Proceedings ArticleDOI
Various Approaches in Deep Learning for Medical Modality Image Segmentation
TL;DR: A comprehensive review of the most recent research in these fields can be found in this article , where the authors assess the performance of different deep learning-based segmentation representation, examine their strengths and drawbacks, and identify promising future avenues.
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.