AtlasNet: Multi-atlas Non-linear Deep Networks for Medical Image Segmentation

By Maria Vakalopoulou, Guillaume Chassagnon, Norbert Bus, Rafael Marini, Evangelia I. Zacharaki, Marie-Pierre Revel, Nikos Paragios. 2018

76 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Medical Imaging

How researchers see this paper

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

In Biomedical Engineering

1. Susan M. Resnick
2. Sebastian Nowozin
3. Andreas Geiger

In Computer Vision and Pattern Recognition

1. Andreas Geiger
2. Michael Niemeyer
3. Luc Van Gool
4. Jitendra Malik
5. Andrea Vedaldi
6. Ming-Hsuan Yang
7. Lars M. Mescheder
8. Dacheng Tao

In Radiology, Nuclear Medicine and Imaging

1. Bennett A. Landman
2. Susan M. Resnick
3. Sebastian Nowozin
4. Andreas Geiger
5. Lars M. Mescheder
6. Christos Davatzikos
7. Arvid Lundervold

Citations in the corpus

• Occupancy Networks: Learning 3D Reconstruction in Function Space (2018, background): “intersecting meshes. Moreover, they are only able to generate meshes with simple topology [54], require a reference template from the same object class [25 ,32 43] or cannot guarantee closed surfaces [53]. Liao et al. [34] proposed an end-to-end learnable version of the marching cubes algorithm [35]”
• 3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles (2019, methodology): “A number of machine learning MALF methods have been developed to learn MAS model from the larger number of atlases [36-41].”