Initial study on the classification of amyotrophic diseases using texture analysis and deep learning in whole-body CT images

2019 ◽  
Author(s):  
Naoki Kamiya ◽  
Ami Oshima ◽  
Erika Asano ◽  
Xiangrong Zhou ◽  
Megumi Yamada ◽  
...  
Keyword(s):  
Deep Learning ◽  
Texture Analysis ◽  
Initial Study ◽  
Ct Images ◽  
Whole Body ◽  
Whole Body Ct ◽  
Body Ct

Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images

2021 ◽  
Author(s):  
Noémie Moreau ◽  
Caroline Rousseau ◽  
Constance Fourcade ◽  
Gianmarco Santini ◽  
Ludovic Ferrer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Whole Body ◽  
Bone Segmentation ◽  
Whole Body Ct ◽  
Body Ct

scholarly journals Surface Muscle Segmentation Using 3D U-Net Based on Selective Voxel Patch Generation in Whole-Body CT Images

2020 ◽  
Vol 10 (13) ◽  
pp. 4477
Author(s):  
Naoki Kamiya ◽  
Ami Oshima ◽  
Xiangrong Zhou ◽  
Hiroki Kato ◽  
Takeshi Hara ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Ct Images ◽  
Whole Body ◽  
Test Case ◽  
Whole Body Ct ◽  
Segmentation Accuracy ◽  
Small Muscle ◽  
Using Data ◽  
Muscle Segmentation ◽  
Body Ct

This study aimed to develop and validate an automated segmentation method for surface muscles using a three-dimensional (3D) U-Net based on selective voxel patches from whole-body computed tomography (CT) images. Our method defined a voxel patch (VP) as the input images, which consisted of 56 slices selected at equal intervals from the whole slices. In training, one VP was used for each case. In the test, multiple VPs were created according to the number of slices in the test case. Segmentation was then performed for each VP and the results of each VP merged. The proposed method achieved a segmentation accuracy mean dice coefficient of 0.900 for 8 cases. Although challenges remain in muscles adjacent to visceral organs and in small muscle areas, VP is useful for surface muscle segmentation using whole-body CT images with limited annotation data. The limitation of our study is that it is limited to cases of muscular disease with atrophy. Future studies should address whether the proposed method is effective for other modalities or using data with different imaging ranges.

Saliency-based rotation invariant descriptor for wrist detection in whole body CT images

2014 ◽  
Author(s):  
Mingchen Gao ◽  
Yiqiang Zhan ◽  
Gerardo Hermosillo ◽  
Yoshihisa Shinagawa ◽  
Dimitris Metaxas ◽  
...  
Keyword(s):  
Ct Images ◽  
Whole Body ◽  
Rotation Invariant ◽  
Whole Body Ct ◽  
Body Ct

Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer

2020 ◽  
Vol 30 (6) ◽  
pp. 3528-3537 ◽  
Author(s):  
Paul Blanc-Durand ◽  
Luca Campedel ◽  
Sébastien Mule ◽  
Simon Jegou ◽  
Alain Luciani ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Small Cell Lung Cancer ◽  
Prognostic Value ◽  
Small Cell ◽  
Whole Body ◽  
Small Cell Lung ◽  
Anthropometric Measures ◽  
Whole Body Ct ◽  
Body Ct

Automatic bone segmentation in whole-body CT images

2018 ◽  
Vol 14 (1) ◽  
pp. 21-29 ◽  
Author(s):  
André Klein ◽  
Jan Warszawski ◽  
Jens Hillengaß ◽  
Klaus H. Maier-Hein
Keyword(s):  
Ct Images ◽  
Whole Body ◽  
Bone Segmentation ◽  
Whole Body Ct ◽  
Body Ct

Segmentation of Skeleton and Organs in Whole-Body CT Images via Iterative Trilateration

2017 ◽  
Vol 36 (11) ◽  
pp. 2276-2286 ◽  
Author(s):  
Marie Bieth ◽  
Loic Peter ◽  
Stephan G. Nekolla ◽  
Matthias Eiber ◽  
Georg Langs ◽  
...  
Keyword(s):  
Ct Images ◽  
Whole Body ◽  
Whole Body Ct ◽  
Body Ct

scholarly journals An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT

2021 ◽  
Vol 34 (1) ◽  
pp. 53-65
Author(s):  
David Dreizin ◽  
Florian Goldmann ◽  
Christina LeBedis ◽  
Alexis Boscak ◽  
Matthew Dattwyler ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pelvic Fracture ◽  
Whole Body ◽  
Learning Method ◽  
Whole Body Ct ◽  
Severity Grading ◽  
Body Ct
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