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 Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes
Changyang Li,Xiuying Wang,Stefan Eberl,Michael J. Fulham,Yong Yin,Jinhu Chen,David Dagan Feng +6 more
TL;DR: A level set model incorporating likelihood energy with the edge energy incorporates the ramp associated with the edges for weak boundaries for hepatic tumor segmentation and outperformed the Chan-Vese and geodesic level set models.
Posted Content
Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation
TL;DR: In this paper, a holistic learning approach that integrates semantic mid-level cues of deeply-learned organ interior and boundary maps via robust spatial aggregation using random forest is presented, which achieves a (mean $\pm$ std. dev.) Dice Similarity Coefficient of 78.01% and 8.2% in testing.
Book ChapterDOI
Interactive Whole-Heart Segmentation in Congenital Heart Disease
Danielle F. Pace,Adrian V. Dalca,Tal Geva,Andrew J. Powell,Andrew J. Powell,Mehdi H. Moghari,Mehdi H. Moghari,Polina Golland +7 more
TL;DR: This work presents an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease, and shows that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short- axis slices.
Journal ArticleDOI
Optimizing boundary detection via Simulated Search with applications to multi-modal heart segmentation
TL;DR: This paper proposes a new method for the local assessment of boundary detection called Simulated Search, which takes any boundary detection function and evaluates its performance for a single model landmark in terms of an estimated geometric boundary detection error and demonstrates the success of the method for cardiac image segmentation.
Journal ArticleDOI
Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms
TL;DR: The computerized liver extraction scheme provides an efficient and accurate way of measuring liver volumes in CT and would require substantially less completion time (compared to an average of 39 min per case by manual segmentation).