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
A Registration-Based Propagation Framework for Automatic Whole Heart Segmentation of Cardiac MRI
TL;DR: This paper proposes a fully automatic whole heart segmentation framework based on two new image registration algorithms: the locally affine registration method (LARM) and the free-form deformations with adaptive control point status (ACPS FFDs).
Book ChapterDOI
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
TL;DR: Wang et al. as mentioned in this paper proposed a 3D deeply supervised network (3D DSN) for liver segmentation from CT volumes, which takes advantage of a fully convolutional architecture and introduces a deep supervision mechanism during the learning process to combat potential optimization difficulties.
Segmentation of Brain Tumor Tissues with Convolutional Neural Networks
TL;DR: In this paper, the authors apply CNNs to segmentation of brain tumor tissues, using multi-channel intensity information from a small patch around each point to be labeled, and only standard intensity pre-processing is applied to the input data to account for scanner differences.
Journal ArticleDOI
SPASM: A 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data
Hans C. van Assen,Mikhail G. Danilouchkine,Alejandro F. Frangi,Sebastian Ordas,Jos J.M. Westenberg,Johan H. C. Reiber,Boudewijn P. F. Lelieveldt +6 more
TL;DR: In this article, a 3D-ASM-based segmentation method was proposed for cardiac MRI image data sets consisting of multiple planes with arbitrary orientations, and with large undersampled regions.
Proceedings Article
Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model
TL;DR: A fully automatic 3D segmentation method for the liver from contrast-enhanced CT data is presented, based on a combination of a constrained free-form and statistical deformable model, considering the potential presence of tumors in the liver.