Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto‐contouring

BACKGROUND: Automatic segmentation of 3D objects in computed tomography (CT) is challenging. Current methods, based mainly on artificial intelligence (AI) and end‐to‐end deep learning (DL) networks, are weak in garnering high‐level anatomic information, which leads to compromised efficiency and robu...

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Detalles Bibliográficos
Autores principales: Udupa, Jayaram K., Liu, Tiange, Jin, Chao, Zhao, Liming, Odhner, Dewey, Tong, Yubing, Agrawal, Vibhu, Pednekar, Gargi, Nag, Sanghita, Kotia, Tarun, Goodman, Michael, Wileyto, E. Paul, Mihailidis, Dimitris, Lukens, John Nicholas, Berman, Abigail T., Stambaugh, Joann, Lim, Tristan, Chowdary, Rupa, Jalluri, Dheeraj, Jabbour, Salma K., Kim, Sung, Reyhan, Meral, Robinson, Clifford G., Thorstad, Wade L., Choi, Jehee Isabelle, Press, Robert, Simone, Charles B., Camaratta, Joe, Owens, Steve, Torigian, Drew A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
QUANTITATIVE IMAGING AND IMAGE PROCESSING
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087050/
https://www.ncbi.nlm.nih.gov/pubmed/35833287
http://dx.doi.org/10.1002/mp.15854

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10087050/
https://www.ncbi.nlm.nih.gov/pubmed/35833287
http://dx.doi.org/10.1002/mp.15854

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