Fully automated imaging protocol independent system for pituitary adenoma segmentation: a convolutional neural network—based model on sparsely annotated MRI-Reference-Cited by-同舟云学术

Fully automated imaging protocol independent system for pituitary adenoma segmentation: a convolutional neural network—based model on sparsely annotated MRI

Published:2023-05-10 Issue:1 Volume:46 Page:
ISSN:1437-2320
Container-title:Neurosurgical Review
language:en
Short-container-title:Neurosurg Rev

Author:

Černý Martin^ORCID,Kybic Jan^ORCID,Májovský Martin^ORCID,Sedlák Vojtěch^ORCID,Pirgl Karin^ORCID,Misiorzová Eva^ORCID,Lipina Radim^ORCID,Netuka David^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Neurology (clinical),General Medicine,Surgery

Link

https://link.springer.com/content/pdf/10.1007/s10143-023-02014-3.pdf

Reference66 articles.

1. Daly AF, Beckers A (2020) The epidemiology of pituitary adenomas. Endocrinol Metab Clin North Am 49(3):347–355. https://doi.org/10.1016/j.ecl.2020.04.002

2. Molitch ME (2017) Diagnosis and treatment of pituitary adenomas: a review. JAMA 317(5):516–524. https://doi.org/10.1001/jama.2016.19699

3. Celtikci E (2018) A systematic review on machine learning in neurosurgery: the future of decision-making in patient care. Turk Neurosurg 28(2):167–173. https://doi.org/10.5137/1019-5149.JTN.20059-17.1

4. Dai C, Sun B, Wang R, Kang J (2021) The application of artificial intelligence and machine learning in pituitary adenomas. Front Oncol 11:784819. https://doi.org/10.3389/fonc.2021.784819

5. Wang H, Zhang W, Li S, Fan Y, Feng M, Wang R (2021) Development and evaluation of deep learning-based automated segmentation of pituitary adenoma in clinical task. J Clin Endocrinol Metab 106(9):2535–2546. https://doi.org/10.1210/clinem/dgab371

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