Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets
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Summary
A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated and demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.
- Type
- article
- Published
- 2016-11-24
- Cited by
- 190
- References
- 39
- OpenAlex
- https://openalex.org/W2555096873
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5391683
Keywords
Convolutional neural network, Computer science, Artificial intelligence, Segmentation, Deep learning
References
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- Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy
- Statistical 4D Graphs for Multi-Organ Abdominal Segmentation from Multiphase CT
- Regression forests for efficient anatomy detection and localization in computed tomography scans
- Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation
- Automatic Initialization of Contour for Level Set Algorithms Guided by Integration of Multiple Views to Segment Abdominal CT Scans
- Some generalized order-disorder transformations
- ImageNet classification with deep convolutional neural networks
- A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
- Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images
- Multi-organ localization with cascaded global-to-local regression and shape prior
- Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs
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- A survey on deep learning in medical image analysis
- Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation
- Development of a deep neural network derived from contours defined by consensus-based guidelines for automatic target segmentation in hepatocellular carcinoma radiotherapy: A study protocol
- Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images
- A variational approach to liver segmentation using statistics from multiple sources
- Localising Anatomical Structures and Quantifying Tumour Burden in PET/CT Images using Machine Learning
- Fully automatic detection of renal cysts in abdominal CT scans
- Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks
- Strategies for prediction and mitigation of radiation-induced liver toxicity
- Organ-specific context-sensitive CT image reconstruction and display
- An application of cascaded 3D fully convolutional networks for medical image segmentation
- Automated Mouse Organ Segmentation: A Deep Learning Based Solution
- Technical Note: A deep learning‐based autosegmentation of rectal tumors in MR images
- Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation
- Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning.
- Survey on deep learning for radiotherapy
- Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut
- Predictive Analytics and Modeling Employing Machine Learning Technology: The Next Step in Data Sharing, Analysis, and Individualized Counseling Explored With a Large, Prospective Prenatal Hydronephrosis Database.
- Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy
- Comparison of level set models in image segmentation
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