Handbook of Medical Image Computing and Computer Assisted Intervention
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offeri...
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| Hlavní autoři: | , , |
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| Médium: | E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Chantilly
Elsevier Science & Technology
2019
Academic Press |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 9780128161760, 0128161760 |
| On-line přístup: | Získat plný text |
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- 3.2.3 Experiments and results -- 3.2.3.1 Data -- 3.2.3.2 Comparative system performance -- 3.3 Fully convolutional network for CT to PET synthesis to augment malignant liver lesion detection -- 3.3.1 Related work -- 3.3.2 Deep learning-based virtual-PET generation -- 3.3.2.1 Training data preparation -- 3.3.2.2 The networks -- 3.3.2.3 SUV-adapted loss function -- 3.3.3 Experiments and results -- 3.3.3.1 Dataset -- 3.3.3.2 Experimental setting -- 3.3.3.3 Liver lesion detection using the virtual-PET -- 3.4 Discussion and conclusions -- Acknowledgments -- References -- 4 CAD in lung -- 4.1 Overview -- 4.2 Origin of lung CAD -- 4.3 Lung CAD systems -- 4.4 Localized disease -- 4.4.1 Lung nodule -- 4.4.1.1 Nodule detection and segmentation -- Hessian-based approach -- Deep learning-based approach -- 4.4.2 Ground Glass Opacity (GGO) nodule -- 4.4.3 Enlarged lymph node -- 4.5 Diffuse lung disease -- 4.5.1 Emphysema -- 4.6 Anatomical structure extraction -- 4.6.1 Airway -- 4.6.2 Blood vessel segmentation in the lung -- 4.6.3 Lung area extraction -- 4.6.4 Lung lobe segmentation -- References -- 5 Text mining and deep learning for disease classi cation -- 5.1 Introduction -- 5.2 Literature review -- 5.2.1 Text mining -- 5.2.2 Disease classi cation -- 5.3 Case study 1: text mining in radiology reports and images -- 5.3.1 Text mining radiology reports -- 5.3.1.1 Architecture -- 5.3.1.1.1 Medical ndings recognition -- 5.3.1.1.2 Universal dependency graph construction -- 5.3.1.1.3 Negation and uncertainty detection -- 5.3.1.2 Evaluation of NegBio -- 5.3.2 ChestX-ray 14 construction -- 5.3.3 Common thoracic disease detection and localization -- 5.3.3.1 Architecture -- 5.3.3.1.1 Uni ed DCNN framework -- 5.3.3.1.2 Weakly-supervised pathology localization -- 5.3.3.2 Evaluation -- 5.4 Case study 2: text mining in pathology reports and images -- 5.4.1 Image model
- Sparsity in global context -- 9.3.2.2 Organ shape initialization and re nement -- Shape initialization using robust model alignment -- Discriminative boundary re nement -- 9.3.2.3 Comparison with other methods -- 9.3.2.4 Experimental results -- 9.4 Conclusion -- References -- 10 Deep multilevel contextual networks for biomedical image segmentation -- 10.1 Introduction -- 10.2 Related work -- 10.2.1 Electron microscopy image segmentation -- 10.2.2 Nuclei segmentation -- 10.3 Method -- 10.3.1 Deep multilevel contextual network -- 10.3.2 Regularization with auxiliary supervision -- 10.3.3 Importance of receptive eld -- 10.4 Experiments and results -- 10.4.1 Dataset and preprocessing -- 10.4.1.1 2012 ISBI EM segmentation -- 10.4.1.2 2015 MICCAI nuclei segmentation -- 10.4.2 Details of training -- 10.4.3 2012 ISBI neuronal structure segmentation challenge -- 10.4.3.1 Qualitative evaluation -- 10.4.3.2 Quantitative evaluation metrics -- 10.4.3.3 Results comparison without postprocessing -- 10.4.3.4 Results comparison with postprocessing -- 10.4.3.5 Ablation studies of our method -- 10.4.4 2015 MICCAI nuclei segmentation challenge -- 10.4.4.1 Qualitative evaluation -- 10.4.4.2 Quantitative evaluation metrics -- 10.4.4.3 Quantitative results and comparison -- 10.4.5 Computation time -- 10.5 Discussion and conclusion -- Acknowledgment -- References -- 11 LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction -- 11.1 Introduction -- 11.2 LOGISMOS -- 11.2.1 Initial mesh -- 11.2.2 Locations of graph nodes -- 11.2.3 Cost function design -- 11.2.4 Geometric constraints and priors -- 11.2.5 Graph optimization -- 11.3 Just-enough interaction -- 11.4 Retinal OCT segmentation -- 11.5 Coronary OCT segmentation -- 11.6 Knee MR segmentation -- 11.7 Modular application design -- 11.8 Conclusion -- Acknowledgments -- References
- Front Cover -- Handbook of Medical Image Computing and Computer Assisted Intervention -- Copyright -- Contents -- Contributors -- Acknowledgment -- 1 Image synthesis and superresolution in medical imaging -- 1.1 Introduction -- 1.2 Image synthesis -- 1.2.1 Physics-based image synthesis -- 1.2.2 Classi cation-based synthesis -- 1.2.3 Registration-based synthesis -- 1.2.4 Example-based synthesis -- 1.2.5 Scan normalization in MRI -- 1.3 Superresolution -- 1.3.1 Superresolution reconstruction -- 1.3.2 Single-image deconvolution -- 1.3.3 Example-based superresolution -- 1.4 Conclusion -- References -- 2 Machine learning for image reconstruction -- 2.1 Inverse problems in imaging -- 2.2 Unsupervised learning in image reconstruction -- 2.3 Supervised learning in image reconstruction -- 2.3.1 Learning an improved regularization function -- Nonconvex regularization -- Bi-level optimization -- Convolutional neural networks as regularization -- 2.3.2 Learning an iterative reconstruction model -- Example: Single-coil MRI reconstruction Schlemper2018 -- 2.3.3 Deep learning for image and data enhancement -- 2.3.4 Learning a direct mapping -- 2.3.5 Example: Comparison between learned iterative reconstruction and learned postprocessing -- 2.4 Training data -- Transfer learning -- 2.5 Loss functions and evaluation of image quality -- 2.6 Discussion -- Acknowledgments -- References -- 3 Liver lesion detection in CT using deep learning techniques -- 3.1 Introduction -- 3.1.1 Prior work: segmentation vs. detection -- 3.1.2 FCN for pixel-to-pixel transformations -- 3.2 Fully convolutional network for liver lesion detection in CT examinations -- 3.2.1 Lesion candidate detection via a fully convolutional network architecture -- 3.2.1.1 FCN candidate generation results -- 3.2.2 Superpixel sparse-based classi cation for false-positives reduction
- 5.4.2 Language model -- 5.4.3 Dual-attention model -- 5.4.4 Image prediction -- 5.4.5 Evaluation -- 5.5 Conclusion and future work -- Acknowledgments -- References -- 6 Multiatlas segmentation -- 6.1 Introduction -- 6.2 History of atlas-based segmentation -- 6.2.1 Atlas generation -- 6.2.2 Preprocessing -- 6.2.3 Registration -- 6.2.3.1 Linear -- 6.2.3.2 Nonlinear -- 6.2.3.3 Label propagation -- 6.2.4 Atlas selection -- 6.2.5 Label fusion -- 6.2.5.1 Voting -- 6.2.5.2 Rater modeling -- 6.2.5.3 Bayesian / generative models -- 6.2.6 Post hoc analysis -- 6.2.6.1 Corrective learning -- 6.2.6.2 EM-re nement -- 6.2.6.3 Markov Random Field (MRF) -- 6.2.6.4 Morphology correction -- 6.3 Mathematical framework -- 6.3.1 Problem de nition -- 6.3.2 Voting label fusion -- 6.3.3 Statistical label fusion -- 6.3.4 Spatially varying performance and nonlocal STAPLE -- 6.3.5 Spatial STAPLE -- 6.3.6 Nonlocal STAPLE -- 6.3.7 Nonlocal spatial STAPLE -- 6.3.8 E-step: estimation of the voxel-wise label probability -- 6.3.9 M-step: estimation of the performance level parameters -- 6.4 Connection between multiatlas segmentation and machine learning -- 6.5 Multiatlas segmentation using machine learning -- 6.6 Machine learning using multiatlas segmentation -- 6.7 Integrating multiatlas segmentation and machine learning -- 6.8 Challenges and applications -- 6.8.1 Multiatlas labeling on cortical surfaces and sulcal landmarks -- 6.9 Unsolved problems -- Glossary -- References -- 7 Segmentation using adversarial image-to-image networks -- 7.1 Introduction -- 7.1.1 Generative adversarial network -- 7.1.2 Deep image-to-image network -- 7.2 Segmentation using an adversarial image-to-image network -- 7.2.1 Experiments -- 7.3 Volumetric domain adaptation with intrinsic semantic cycle consistency -- 7.3.1 Methodology -- 7.3.1.1 3D dense U-Net for left atrium segmentation
- 12 Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics
- 7.3.1.2 Volumetric domain adaptation with cycle consistency -- 7.3.2 Experiments -- 7.3.3 Conclusions -- References -- 8 Multimodal medical volumes translation and segmentation with generative adversarial network -- 8.1 Introduction -- 8.2 Literature review -- 8.2.1 Medical image synthesis -- 8.2.2 Image segmentation -- 8.3 Preliminary -- 8.3.1 CNN for segmentation -- 8.3.2 Generative adversarial network -- 8.3.3 Image-to-image translation for unpaired data -- 8.3.4 Problems in unpaired volume-to-volume translation -- 8.4 Method -- 8.4.1 Volume-to-volume cycle consistency -- 8.4.2 Volume-to-volume shape consistency -- 8.4.3 Multimodal volume segmentation -- 8.4.4 Method objective -- 8.5 Network architecture and training details -- 8.5.1 Architecture -- 8.5.2 Training details -- 8.6 Experimental results -- 8.6.1 Dataset -- 8.6.2 Cross-domain translation evaluation -- 8.6.3 Segmentation evaluation -- 8.6.4 Gap between synthetic and real data -- 8.6.5 Is more synthetic data better? -- 8.7 Conclusions -- References -- 9 Landmark detection and multiorgan segmentation: Representations and supervised approaches -- 9.1 Introduction -- 9.2 Landmark detection -- 9.2.1 Landmark representation -- 9.2.1.1 Point-based representation -- 9.2.1.2 Relative offset representation -- 9.2.1.3 Identity map representation -- 9.2.1.4 Distance map representation -- 9.2.1.5 Heat map representation -- 9.2.1.6 Discrete action map representation -- 9.2.2 Action classi cation for landmark detection -- 9.2.2.1 Method -- 9.2.2.2 Dataset & -- experimental setup -- 9.2.2.3 Qualitative and quantitative results -- 9.3 Multiorgan segmentation -- 9.3.1 Shape representation -- 9.3.2 Context integration for multiorgan segmentation -- 9.3.2.1 Joint landmark detection using context integration -- Local context posterior -- Global context posterior -- MMSE estimate for landmark location