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
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
TL;DR: UNet++ as mentioned in this paper proposes an efficient ensemble of U-Nets of varying depths, which partially share an encoder and co-learn simultaneously using deep supervision, leading to a highly flexible feature fusion scheme.
Journal ArticleDOI
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
TL;DR: A critical appraisal of popular methods that have employed deep learning techniques for medical image segmentation is presented and the most common challenges incurred are summarized and suggest possible solutions.
Posted Content
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation.
TL;DR: A Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual convolutional neural Network (RRCNN), which are named RU-Net and R2U-Net respectively are proposed, which show superior performance on segmentation tasks compared to equivalent models including U-nets and residual U- net.
Journal ArticleDOI
Applications of Deep Learning and Reinforcement Learning to Biological Data
TL;DR: This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data and compares the performances of DL techniques when applied to different data sets across various application domains.
Journal ArticleDOI
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Guotai Wang,Wenqi Li,Maria A. Zuluaga,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren +10 more
TL;DR: A novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline and proposing a weighted loss function considering network and interaction-based uncertainty for the fine tuning is proposed.
References
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Journal ArticleDOI
Statistical shape models for 3D medical image segmentation: a review.
TL;DR: Statistical shape models (SSMs) have by now been firmly established as a robust tool for segmentation of medical images as discussed by the authors, primarily made possible by breakthroughs in automatic detection of shape correspondences.
Proceedings Article
Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images
TL;DR: This work addresses a central problem of neuroanatomy, namely, the automatic segmentation of neuronal structures depicted in stacks of electron microscopy images, using a special type of deep artificial neural network as a pixel classifier to segment biological neuron membranes.
Proceedings Article
Deeply-Supervised Nets
TL;DR: Deeply-supervised nets (DSN) as discussed by the authors is a method that simultaneously minimizes classication error and improves the directness and transparency of the hidden layer learning process by introducing companion objective functions at each hidden layer, in addition to the overall objective function at the output layer.
Posted Content
ParseNet: Looking Wider to See Better
TL;DR: This work presents a technique for adding global context to deep convolutional networks for semantic segmentation, and achieves state-of-the-art performance on SiftFlow and PASCAL-Context with small additional computational cost over baselines.
Posted Content
Deeply-Supervised Nets
TL;DR: Deeply-supervised nets (DSN) as discussed by the authors proposes a companion objective to the individual hidden layers, in addition to the overall objective at the output layer, which is a different strategy to layer-wise pre-training.