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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A Statistical Deformable Model for the Segmentation of Liver CT Volumes
TL;DR: A fully automated method based on an evolu- tionary algorithm, a statistical shape model (SSM), and a deformable mesh to tackle the liver segmentation task of the MICCAI Grand Chal- lenge workshop.
Book ChapterDOI
A Generic Probabilistic Active Shape Model for Organ Segmentation
TL;DR: This work proposes a novel probabilistic active shape model for organ segmentation, which is entirely built upon non-parametric density estimates, and complemented by a cascade of boosted classifiers for region information and combined with a shape model based on Parzen density estimation.
Posted Content
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
TL;DR: This paper proposes a multi-domain regularized deep learning method that achieves a superior detection and segmentation accuracy, outperforming other methods by a significant margin and demonstrating competitive capability even compared to human performance.
Book ChapterDOI
Dense Volume-to-Volume Vascular Boundary Detection
TL;DR: This work introduces HED-3D, a 3D extension of the state-of-the-art 2D edge detector (HED), and develops a novel 3D-Convolutional Neural Network (CNN) architecture, I2I- 3D, that predicts boundary location in volumetric data.
Book ChapterDOI
Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images
TL;DR: Wang et al. as discussed by the authors proposed a multi-domain regularized deep learning method to address the challenging problem of accurate detection and segmentation of anatomical structures from ultrasound images, which leveraged the transfer learning from cross domains, the feature representations are effectively enhanced.