scholarly journals Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images

2018 ◽  
Vol 45 (5) ◽  
pp. 2063-2075 ◽  
Author(s):  
Xuhua Ren ◽  
Lei Xiang ◽  
Dong Nie ◽  
Yeqin Shao ◽  
Huan Zhang ◽  
...  
Keyword(s):  
Head And Neck ◽  
Small Volume ◽  
Ct Images

scholarly journals Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Wen Chen ◽  
Yimin Li ◽  
Brandon A. Dyer ◽  
Xue Feng ◽  
Shyam Rao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Head And Neck ◽  
Masticatory Muscles ◽  
Ct Images

scholarly journals A Multi-atlas Approach for the Automatic Segmentation of Multiple Structures in Head and Neck CT Images

2016 ◽  
Author(s):  
Antong Chen ◽  
Benoit Dawant
Keyword(s):  
Head And Neck ◽  
Local Level ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Training Dataset ◽  
Parotid Glands ◽  
Label Fusion ◽  
Qualitative And Quantitative ◽  
Segmentation Accuracy ◽  
Multiple Structures

A multi-atlas approach is proposed for the automatic segmentation of nine different structures in a set of head and neck CT images for radiotherapy. The approach takes advantage of a training dataset of 25 images to build average head and neck atlases of high-quality. By registering patient images with the atlases at the global level, structures of interest are aligned approximately in space, which allowed multi-atlas-based segmentations and correlation-based label fusion to be performed at the local level in the following steps. Qualitative and quantitative evaluations are performed on a set of 15 testing images. As shown by the results, mandible, brainstem and parotid glands are segmented accurately (mean volume DSC>0.8). The segmentation accuracy for the optic nerves is also improved over previously reported results (mean DSC above 0.61 compared with 0.52 for previous results).

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