Fully automated prostate whole gland and central gland segmentation on MRI using holistically nested networks with short connections

Accurate and automated prostate whole gland and central gland segmentations on MR images are essential for aiding any prostate cancer diagnosis system. Our work presents a 2-D orthogonal deep learning method to automatically segment the whole prostate and central gland from T2-weighted axial-only MR...

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Bibliografische gegevens
Gepubliceerd in:J Med Imaging (Bellingham)
Hoofdauteurs: Cheng, Ruida, Lay, Nathan, Roth, Holger R., Turkbey, Baris, Jin, Dakai, Gandler, William, McCreedy, Evan S., Pohida, Tom, Pinto, Peter, Choyke, Peter, McAuliffe, Matthew J., Summers, Ronald M.
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Society of Photo-Optical Instrumentation Engineers 2019
Onderwerpen:
Image Processing
Online toegang:https://ncbi.nlm.nih.gov/pmc/articles/PMC6551111/
https://ncbi.nlm.nih.gov/pubmed/31205977
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1117/1.JMI.6.2.024007
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