Interleaved 3D-CNNs for Joint Segmentation of Small-Volume Structures in Head and Neck CT Images-筆記
傳統分割： （1）Atlas based methods, （2）Statistical shape/appearance based methods （3）Classification based methods 論文方法： 1.調整窗寬窗位爲[-200，200]。（肉眼可以觀察軟組織器官） 2.採用MABS method方法粗定位ROIs。使用歸一化互信息指導配準。配準包含基於非線性變形的仿射
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相關文章
- 1. Segmentation簡記-Joint shape learning and segmentation for medical images
- 2. 論文筆記 之 Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow
- 3. 論文筆記_2018-ECCV-Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation
- 4. Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation筆記
- 5. 論文筆記_2010-BMVC-Joint optimization for object class segmentation and dense stereo reconstruction
- 6. 【閱讀筆記】《Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network》（JSIS-Net）
- 7. 【論文筆記】Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations
- 8. 論文筆記 Joint Inference of Reward Machines and Policies for Reiforcement Learning
- 9. 【論文閱讀筆記】Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Net
- 10. 分割網絡綜述——Deep Semantic Segmentation of Natural and Medical Images: A Review