1. |
纪祥虎, 高思聪, 黄志标, 等. 基于 Centripetal Catmull-Rom 曲线的经食道超声心动图左心室分割方法. 四川大学学报: 工程科学版, 2016, 48(5): 84-90.
|
2. |
孙申申, 范立南, 康雁, 等. 基于改进主动形状模型的含胸壁粘连型肿块的肺区分割方法研究. 生物医学工程学杂志, 2016(5): 879-884.
|
3. |
Carneiro G, Nascimento J C, Freitas A. The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Transactions on Image Processing, 2012, 21(3): 968-982.
|
4. |
Milletari F, Ahmadi S A, Kroll C A, et al. Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Computer Vision and Image Understanding, 2017, 164(SI): 92-102.
|
5. |
Gao Mingchen, Xu Ziyue, Lu Le, et al. Segmentation label propagation using deep convolutional neural networks and dense conditional random field//2016 IEEE 13th international symposium on biomedical imaging (ISBI), 2016: 1265-1268.
|
6. |
Mignotte M, Meunier J, Tardif J C. Endocardial boundary e timation and tracking in echocardiographic images using deformable template and markov random fields. Pattern Analysis Applications, 2001, 4(4): 256-271.
|
7. |
Mishra A, Dutta P K, Ghosh M K. A GA based approach for boundary detection of left ventricle with echocardiographic image sequences. Image Vis Comput, 2003, 21(11): 967-976.
|
8. |
Sadek I, Elawady M, Stefanovski V. Automated breast lesion segmentation in ultrasound images. arXiv preprint. 2016. arXiv: 1609.08364.
|
9. |
McClymont D, Mehnert A, Trakic A, et al. Fully automatic lesion segmentation in breast MRI using Mean-shift and graph-cuts on a region adjacency graph. Journal of Magnetic Resonance Imaging, 2014, 39(4): 795-804.
|
10. |
ZHOU Zhuhuang, WU Weiwei, WU Shuicai, et al. Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts. Ultrason Imaging, 2014, 36(4): 256-276.
|
11. |
Kim J H, Lee S, Lee G S, et al. Using a method based on a modified K-Means clustering and mean shift segmentation to reduce file sizes and detect brain tumors from magnetic resonance (MRI) images. Wireless Personal Communications, 2016, 89(3, SI): 993-1008.
|
12. |
Mayer A, Greenspan H. An adaptive Mean-Shift framework for MRI brain segmentation. IEEE Trans Med Imaging, 2009, 28(8): 1238-1250.
|
13. |
Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell, 2002, 24(5): 603-619.
|
14. |
Ren Shaoqing, He Kaiming, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks//Computer Vision and Pattern Recognition, 2015: 91-99.
|
15. |
Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation//Proceedings of the IEEE conference on computer vision and pattern recognition, 2014: 580-587.
|
16. |
Girshick R. Fast R-CNN//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
|
17. |
Zeiler M D, Fergus R. Visualizing and understanding convolutional networks//Computer Vision–ECCV 2014, Zurich, 2014: 818-833.
|
18. |
Deng Jia, Dong Wei, Socher R, et al. Imagenet: a large-scale hierarchical image database//Computer Vision and Pattern Recognition, Miami, 2009: 248-255.
|