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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Liver segmentation from computed tomography scans: A survey and a new algorithm
TL;DR: The gray-level based liver segmentation method has been developed to tackle all problems; when tested on 40 patients it achieves satisfactory results, comparable to the mean intra- and inter-observer variation.
Journal ArticleDOI
Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function
TL;DR: Simulation of diastolic LV mechanics allowed us to estimate the stiffness of the passive LV myocardium based on kinematic data obtained from tagged MRI, thereby improving the understanding of the underlying structural basis of mechanical dysfunction under pathological conditions.
Proceedings ArticleDOI
Hierarchical, learning-based automatic liver segmentation
Haibin Ling,Shaohua Kevin Zhou,Yefeng Zheng,Bogdan Georgescu,Michael Suehling,Dorin Comaniciu +5 more
TL;DR: This paper presents a hierarchical, learning-based approach for automatic and accurate liver segmentation from 3D CT volumes that come from largely diverse sources and are generated by different scanning protocols.
Journal ArticleDOI
Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images
TL;DR: In order to accurately detect the liver surface, deformable graph cut was proposed, which effectively integrates the properties and inter-relationship of the input images and initialized surface.
Journal ArticleDOI
Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets
Catalina Tobon-Gomez,A. J. Geers,Jochen Peters,Jürgen Weese,Karen Pinto,Rashed Karim,Mohammed Ammar,Abdelaziz Daoudi,Jan Margeta,Zulma Sandoval,Birgit Stender,Yefeng Zheng,Maria A. Zuluaga,Julian Betancur,Nicholas Ayache,Mohammed Amine Chikh,Jean-Louis Dillenseger,B. Michael Kelm,Saïd Mahmoudi,Sebastien Ourselin,Alexander Schlaefer,Tobias Schaeffter,Reza Razavi,Kawal Rhode +23 more
TL;DR: A standardisation framework can be used to label and further analyse anatomical regions of the LA by performing the standardisation directly on the left atrial surface, including meshes exported from different electroanatomical mapping systems.