IC-P-039: Influence of the training library composition on a patch-based label fusion method: Application to hippocampus segmentation on the ADNI dataset

2011 ◽  
Vol 7 ◽  
pp. S24-S24 ◽  
Author(s):  
Pierrick Coupe ◽  
Vladimir Fonov ◽  
Simon Eskildsen ◽  
José Manjón ◽  
Douglas Arnold ◽  
...  
Author(s):  
Wenna Wang ◽  
Xiuwei Zhang ◽  
Yu Ma ◽  
Hengfei Cui ◽  
Rui Xia ◽  
...  

2011 ◽  
Vol 7 ◽  
pp. S316-S317 ◽  
Author(s):  
Pierrick Coupe ◽  
Vladimir Fonov ◽  
Simon Eskildsen ◽  
José Manjón ◽  
Douglas Arnold ◽  
...  

2017 ◽  
Vol 62 (9) ◽  
pp. 3656-3667 ◽  
Author(s):  
Jina Chang ◽  
Zhen Tian ◽  
Weiguo Lu ◽  
Xuejun Gu ◽  
Mingli Chen ◽  
...  

2017 ◽  
Vol 11 (7) ◽  
pp. 502-511 ◽  
Author(s):  
Hong Liu ◽  
Meng Yan ◽  
Enmin Song ◽  
Yuejing Qian ◽  
Xiangyang Xu ◽  
...  

2019 ◽  
Vol 6 (01) ◽  
pp. 1
Author(s):  
David Cárdenas-Peña ◽  
Andres Tobar-Rodríguez ◽  
German Castellanos-Dominguez ◽  
Alzheimer’s Disease Neuroimaging Initiative

2016 ◽  
Author(s):  
Mauricio Orbes Arteaga ◽  
David Cárdenas Peña ◽  
German Castellanos Dominguez

A new patch based label fusion method based on generative approach is proposed for segmentation of mandible, brainstem, parotid and submandibular glands, optic nerves and the optic chiasm in head and neck CT images. The proposal constructs local classifiers from a dictionary of patches and weights their contribution using a generative probabilistic criterion. Also, a gaussian slide window is used to weight the multiples estimations of neighboring voxels. The proposed method was evaluated on a set of 15 CT images (10 off-site and 5 onsite) provided by the organizers of the Head and neck Auto-Segmentation challenge(MICCAI 2015), where the obtained results are comparable to many of the other methods used in the challenge.


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