Medical image computing and computer assisted intervention -- MICCAI 2019 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings. Part VI /
The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented w...
| Corporate Authors: | International Conference on Medical Image Computing and Computer-Assisted Intervention Shenzhen Shi, China) |
|---|---|
| Other Authors: | International Conference on Medical Image Computing and Computer-Assisted Intervention, Shen, Dinggang,, Liu, Tianming, Dr., Peters, Terry M., 1948 January 5-, Staib, Lawrence,, Essert, Caroline,, Zhou, Xiangyun Sean,, Yap, Pew-Thian,, Khan, Ali,, SpringerLink (Online service) |
| Format: | eBook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
2019.
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| Physical Description: |
1 online resource (xxxviii, 860 pages) : illustrations (some color). |
| Series: |
Lecture notes in computer science ;
11769. LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics. |
| Subjects: |
Table of Contents:
- Intro; Preface; Organization; Accepted MICCAI 2019 Papers; Awards Presented at MICCAI 2018, Granada, Spain; Contents
- Part VI; Computed Tomography; Multi-scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma; 1 Introduction; 2 The Segmentation-for-Classification Approach; 2.1 The Overall Framework; 2.2 Training: Multi-scale Deeply-Supervised Segmentation; 2.3 Testing: Coarse-to-Fine Segmentation with Post-processing; 3 Experiments; 3.1 Dataset and Settings; 3.2 Segmentation Results; 3.3 Classification Results; 4 Conclusion; References.
- MVP-Net: Multi-view FPN with Position-Aware Attention for Deep Universal Lesion Detection1 Introduction; 2 Methodology; 2.1 Multi-view FPN; 2.2 Attention Based Feature Aggregation; 2.3 Position-Aware Modeling; 3 Experiments; 3.1 Experimental Setup; 3.2 Comparison with State-of-the-Arts; 3.3 Ablation Study; 4 Conclusion; References; Spatial-Frequency Non-local Convolutional LSTM Network for pRCC Classification; 1 Introduction; 2 Methodology; 2.1 Overview; 2.2 Spatial-Frequency Non-local Convolutional LSTM Network Architecture; 3 Experiments and Results; 3.1 Dataset; 3.2 Pre-processing.
- 3.3 Implementation Details3.4 Results; 4 Conclusion; References; BCD-Net for Low-Dose CT Reconstruction: Acceleration, Convergence, and Generalization; 1 Introduction; 2 BCD-Net for Low-Dose CT Reconstruction; 2.1 Architecture; 2.2 Training BCD-Net; 2.3 Convergence Analysis; 2.4 Computational Complexity; 3 Experimental Results and Discussion; 3.1 Experimental Setup; 3.2 Results and Discussion; 4 Conclusions; References; Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning; 1 Introduction; 2 Methods; 2.1 Dataset; 2.2 Data Augmentation.
- 2.3 Value Loss and Cross Entropy Loss Function3 Experiments and Results; 3.1 Evaluation Metrics; 3.2 Semi-supervised Algorithm; 3.3 Double Loss Function Collaborative Training; 4 Conclusion and Discussion; References; Closing the Gap Between Deep and Conventional Image Registration Using Probabilistic Dense Displacement Networks; 1 Introduction and Related Work; 2 Methods; 3 Experimental Validation; 4 Results and Discussions; 5 Conclusion; References; Generating Pareto Optimal Dose Distributions for Radiation Therapy Treatment Planning; Abstract; 1 Introduction; 2 Methods.
- 2.1 Prostate Patient Data and Pareto Plan Generation2.2 Deep Learning Architecture; 2.3 Training and Evaluation; 3 Results; 4 Discussion and Conclusion; References; PAN: Projective Adversarial Network for Medical Image Segmentation; 1 Introduction; 2 Method; 2.1 Adversarial Training; 2.2 Segmentor (S); 2.3 Adversarial Networks; 3 Experiments and Results; 4 Conclusion; References; Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction; 1 Introduction; 2 Methodology; 3 Experimental Evaluations; 4 Conclusion; References.