Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Parth Natekar"'
Autor:
Zichen Wang, Parth Natekar, Challana Tea, Sharon Tamir, Hiroyuki Hakozaki, Johannes Schöneberg
Publikováno v:
PLoS Computational Biology, Vol 19, Iss 4, p e1011060 (2023)
Mitochondria form a network in the cell that rapidly changes through fission, fusion, and motility. Dysregulation of this four-dimensional (4D: x,y,z,time) network is implicated in numerous diseases ranging from cancer to neurodegeneration. While lat
Externí odkaz:
https://doaj.org/article/e194d5d8d5124852b91c54a435a4aa07
Publikováno v:
Frontiers in Computational Neuroscience, Vol 15 (2021)
Externí odkaz:
https://doaj.org/article/e9255fe12a494d77aec180672f74fe8b
Publikováno v:
Frontiers in Computational Neuroscience, Vol 14 (2020)
The accurate automatic segmentation of gliomas and its intra-tumoral structures is important not only for treatment planning but also for follow-up evaluations. Several methods based on 2D and 3D Deep Neural Networks (DNN) have been developed to segm
Externí odkaz:
https://doaj.org/article/cf7ecc8787ac4f1d88c8431a4a7c2075
Publikováno v:
AI for Disease Surveillance and Pandemic Intelligence ISBN: 9783030930790
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b1fd0fe9ca5fc001a95b4827333e48b
https://doi.org/10.1007/978-3-030-93080-6_15
https://doi.org/10.1007/978-3-030-93080-6_15
Autor:
Zichen Wang, Parth Natekar, Challana Tea, Sharon Tamir, Hiroyuki Hakozaki, Johannes Schöneberg
Publikováno v:
Biophysical Journal. 122:98a
Autor:
Gillian McMahon, McKenna Rude, Challana Tea, Zichen Wang, Parth Natekar, Hiroyuki Hakozaki, Johannes Schöneberg
Publikováno v:
Biophysical Journal. 122:303a
Publikováno v:
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience, Vol 15 (2021)
Frontiers in Computational Neuroscience, Vol 15 (2021)
Publikováno v:
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience, Vol 14 (2020)
Frontiers in Computational Neuroscience, Vol 14 (2020)
The accurate automatic segmentation of gliomas and its intra-tumoral structures is important not only for treatment planning but also for follow-up evaluations. Several methods based on 2D and 3D Deep Neural Networks (DNN) have been developed to segm