Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation

Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more accurate and user independent. We propose a deep...

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Detalles Bibliográficos
Autores principales: Lo, Justin, Nithiyanantham, Saiee, Cardinell, Jillian, Young, Dylan, Cho, Sherwin, Kirubarajan, Abirami, Wagner, Matthias W., Azma, Roxana, Miller, Steven, Seed, Mike, Ertl-Wagner, Birgit, Sussman, Dafna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272176/
https://www.ncbi.nlm.nih.gov/pubmed/34209154
http://dx.doi.org/10.3390/s21134490

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8272176/
https://www.ncbi.nlm.nih.gov/pubmed/34209154
http://dx.doi.org/10.3390/s21134490

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