Automatic segmentation of pelvic structures from magnetic resonance images for prostate cancer radiotherapy.

By D. Pasquier, T. Lacornerie, M. Vermandel, J. Rousseau, E. Lartigau, Nacim Betrouni. 2007

152 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, Radiation, Medical Imaging, Medical & Health AI

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Geert J. S. Litjens
2. B. van Ginneken
3. Christos Davatzikos
4. Robin Strand
5. Jing Yuan
6. Anant Madabhushi
7. F. Heijden
8. Dean C. Barratt

In Pulmonary and Respiratory Medicine

1. B. van Ginneken
2. Christos Davatzikos

In Medical Imaging

1. Geert J. S. Litjens
2. Anant Madabhushi

In Medical & Health AI

1. Christos Davatzikos
2. B. van Ginneken

Citations in the corpus

• Iterative Interaction Training for Segmentation Editing Networks (2018, neutral): “Segmentation is one of the main medical image analysis tasks that when automated substantially facilitates morphological measurements and increase effi-ciency in treatment planning [11,17,18].”
• A qualitative and a quantitative analysis of an auto-segmentation module for prostate cancer. (2009, methodology): “Prostate (11) 9% 1 [0,2] 45% 3 [1,7] 30% 7 [4,12] 15% 9 [8,13]”
• Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge (2013, methodology): “Comparing these metric scores to scores reported in literature for inter-observer variability we can see that they are at approximately at the same level (Pasquier et al., 2007; Costa et al., 2007; Klein et al., 2008; Makni et al., 2009; Toth et al., 2011b; Chandra et al., 2012; Gao et al., 2012b).”