A framework for evaluating image segmentation algorithms

By Jayaram Koteshwar Udupa, Vicki R. LeBlanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. Currie, B. Hirsch, James Woodburn. Comput. Medical Imaging Graph., 2006

443 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/512ac137e391e440e3bc0a21292831af9a11a7b7

How researchers see this paper

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In Computer Vision and Pattern Recognition

1. Daniel J. Mollura
2. Allan Hanbury
3. Ziyue Xu
4. Abdel Aziz Taha
5. Septimiu E. Salcudean
6. Davood Karimi
7. Davide Chicco
8. Giuseppe Jurman

Citations in the corpus

• A Review on Segmentation of Positron Emission Tomography Images (2014, methodology): “In delineation, the second step of segmentation, the aim is precise separation of uptake regions from the background and non-significant uptakes [43,46,47].”
• Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool (2015, methodology): “However these two measures are not common as evaluation measures of medical image segmentation because of their sensibility to segments size, i.e. they penalize errors in small segments more than in large segments [6, 8, 10].”