Deformable M-Reps for 3D Medical Image Segmentation

By S. Pizer, P. T. Fletcher, Sarang C. Joshi, Andrew Thall, James Z. Chen, Y. Fridman, D. Fritsch, A. G. Gash, J. Glotzer, M. Jiroutek and 5 more. International Journal of Computer Vision, 2003

382 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1395ef893da051665872939531b87b207260f1c8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Zhuowen Tu
2. A. King
3. P. T. Fletcher
4. Xiang Bai
5. Longin Jan Latecki
6. Sarang C. Joshi
7. Xinggang Wang

In Computer Vision and Pattern Recognition

1. Xiang Bai
2. Xinggang Wang
3. Luc Van Gool
4. Zhuowen Tu
5. Chunhua Shen
6. Alan L. Yuille
7. Jitendra Malik
8. Cong Yao

Citations in the corpus

• Digital Topology and Geometry in Medical Imaging: A Survey (2015, neutral): “[38] introduced deformable m-reps,”
• Active skeleton for non-rigid object detection (2009, background): “The idea of using skeleton (medial axis) to model the object shape has been extensively studied in the literature [6, 2, 36, 24, 19, 21, 18].”
• Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation (2010, background): “These algorithms range from shape-driven [54], [30], atlas and knowledge-based [35], Markov Random Fields models [10], [31], to classification/learning-based approaches [24].”
• Principal geodesic analysis for the study of nonlinear statistics of shape (2004, methodology): “Following that point of view, in our previous work [14], [15] we have developed methodology based on medial descriptions called m-reps.”
• A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology (2019, methodology): “While shape models have a long history in medical image analysis [39] traditional methods require solving an optimisation problem at inference time.”
• Statistical shape models for 3D medical image segmentation: A review (2009, methodology): “This approach was later extended to 3D in (Pizer et al., 2003) and termed m-rep.”