论文阅读：Touching Tooth Segmentation from CT Image Sequences Using Coupled Level Set Method
2017-05-25 16:06
417 查看
【论文信息】
Touching Tooth Segmentation from CT Image Sequences Using Coupled Level Set Method
Gao 2008
【背景】
Touching Tooth Segmentation from CT Image Sequences Using Coupled Level Set Method
Gao 2008
【背景】
内容来自用户分享和网络整理，不保证内容的准确性，如有侵权内容，可联系管理员处理 
相关文章推荐
- 论文阅读：Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible te
- 论文阅读：Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using wat
- 论文阅读：Segmentation of teeth in CT volumetric dataset by panoramic projection and variational level se
- 论文阅读：3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images
- Robust Image Segmentation using Active Contours: Level Set Approaches
- 论文阅读：Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks
- 论文阅读：Fast and Accurate Semiautomatic Segmentation of Individual Teeth from Dental CT Images
- 论文阅读：Tooth and Alveolar Bone Segmentation from Dental Computed Tomography Images
- 论文阅读：ROBUST AND FULLY AUTOMATED SEGMENTATION OF MANDIBLE FROM CT SCANS
- 论文阅读笔记-Segmentation-Aware Convolutional Networks Using Local Attention Masks
- 阅读论文：Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 谷歌论文阅读：Building High-level Features Using Large Scale Unsupervised Learning
- 论文阅读-《Joint Object and Part Segmentation using Deep Learned Potentials》
- Medical image fusion using multi-level local extrema论文matlab程序
- Attention to Scale: Scale-Aware Semantic Image Segmentation论文阅读笔记
- [论文阅读笔记]U-Net: Convolutional Networks for Biomedical Image Segmentation
- 论文阅读理解 - Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs
- ICCV2017论文“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks” 阅读笔记
- 论文阅读：Crown Segmentation From Computed Tomography Images With Metal Artifacts
- 论文阅读《Rethinking Atrous Convolution for Semantic Image Segmentation》