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Multi-class segmentation, Auto-context,
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2011
Image segmentation, Feature extraction, Semantics, Training,
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Image segmentation, Kernel, Convolution, Labeling, Cranial,
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Body Part Regression With Self-Supervision,
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IEEE DOI
2105
Computed tomography, Manuals, Unsupervised learning, Training,
Navigation, Task analysis,
multi-organ segmentation
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Elsevier DOI
2109
Survey, Segmentation.
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medical image processing, transforms
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IEEE DOI
2310
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Federated Multi-Organ Segmentation With Inconsistent Labels,
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2310
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DOI Link
2310
abdomen CT, auto-contouring, deep learning, generative models,
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Gao, R.Q.[Ri-Qiang],
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2405
Task analysis, Training, Image segmentation, Biological systems, Annotations,
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2401
attention mechanism, convolutional neural networks,
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2411
Biomedical imaging, Image segmentation, Training,
Contrastive learning, Anatomy, Tail, Image reconstruction,
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Exploring Inherent Consistency for Semi-Supervised Anatomical
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IEEE DOI
2411
Image segmentation, Anatomical structure, Task analysis,
Biomedical imaging, Data models, Training, Predictive models,
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2506
Low-rank adaptive, Weighted attention, Feature fusion,
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Wang, J.J.[Jia-Jun],
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2506
Transformers, Feature extraction, Computed tomography,
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DSC-Recon: Dual-Stage Complementary 4-D Organ Reconstruction From
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2411
X-ray imaging, Shape, Image reconstruction, Computed tomography,
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DAUNet: A deformable aggregation UNet for multi-organ 3D medical
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Deformable aggregation module, Fourier attention module,
Medical image segmentation, Deep learning
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Medical anatomy segmentation.
Convolution, Image segmentation, Anatomy, Strips, Morphology,
Medical diagnostic imaging, Ribs, Shape, Kernel, Lungs,
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Springer DOI
2506
Unpaired multimodal medical image, Cross-modality generative,
Semi-supervised medical image segmentation, Generative adversarial network,
Deep learning
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Anatomask: Enhancing Medical Image Segmentation with
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2412
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2211
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IPTA20(1-6)
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2206
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Cycle GAN-Based Data Augmentation For Multi-Organ Detection In CT
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ICIP20(390-393)
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Computed tomography, Magnetic resonance imaging,
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Semantic Segmentation of Sheep Organs by Convolutional Neural
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IVCNZ19(1-5)
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biological organs, biology computing,
convolutional neural nets, image segmentation, zoology,
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Multiple-organ segmentation by graph cuts with supervoxel nodes,
MVA17(424-427)
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Biomedical imaging, Computed tomography, Image segmentation,
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Biomedical imaging
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Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Weakly Supervised, Self Supervised Semantic Segmentation .