Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views.
Guotai Wang,Maria A. Zuluaga,Rosalind Pratt,Michael Aertsen,Tom Doel,Maria Klusmann,Anna L. David,Jan Deprest,Tom Vercauteren,Sebastien Ourselin +9 more
Reads0
Chats0
TLDR
Minimal user interaction is needed for a good segmentation of the placenta and co-segmentation of multiple volumes outperforms single sparse volume based method.About:
This article is published in Medical Image Analysis.The article was published on 2016-12-01 and is currently open access. It has received 63 citations till now. The article focuses on the topics: Scale-space segmentation & Segmentation.read more
Citations
More filters
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.
Journal ArticleDOI
Medical image analysis
Baba C. Vemuri,James S. Duncan +1 more
TL;DR: Medical imaging systems: Physical principles and image reconstruction algorithms for magnetic resonance tomography, ultrasound and computer tomography (CT), and applications: Image enhancement, image registration, functional magnetic resonance imaging (fMRI).
Journal ArticleDOI
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Guotai Wang,Maria A. Zuluaga,Wenqi Li,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren +10 more
TL;DR: A deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.
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: In this article, the authors proposed a novel deep learning-based framework for interactive segmentation by incorporating CNNs into a bounding box and scribble-based segmentation pipeline.
Journal ArticleDOI
Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges.
Muhammad Waqas Nadeem,Mohammed A. Al Ghamdi,Muzammil Hussain,Muhammad Adnan Khan,Khalid Masood Khan,Sultan H. Almotiri,Suhail Ashfaq Butt +6 more
TL;DR: A review conducted by summarizing a large number of scientific contributions to the field of deep learning in brain tumor analysis is presented, and a coherent taxonomy of research landscape from the literature has been mapped.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
Paul A. Yushkevich,Joseph Piven,Heather C. Hazlett,Rachel Gimpel Smith,Sean Ho,James C. Gee,Guido Gerig +6 more
TL;DR: The methods and software engineering philosophy behind this new tool, ITK-SNAP, are described and the results of validation experiments performed in the context of an ongoing child autism neuroimaging study are provided, finding that SNAP is a highly reliable and efficient alternative to manual tracing.
Journal ArticleDOI
Nonrigid registration using free-form deformations: application to breast MR images
TL;DR: The results clearly indicate that the proposed nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
Journal ArticleDOI
Snakes, shapes, and gradient vector flow
Chenyang Xu,Jerry L. Prince +1 more
TL;DR: This paper presents a new external force for active contours, which is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image, and has a large capture range and is able to move snakes into boundary concavities.