Automated methods for sella turcica segmentation on cephalometric radiographic data using deep learning (CNN) techniques-Reference-Cited by-同舟云学术

Automated methods for sella turcica segmentation on cephalometric radiographic data using deep learning (CNN) techniques

Published:2022-06-23 Issue:2 Volume:39 Page:248-265
ISSN:0911-6028
Container-title:Oral Radiology
language:en
Short-container-title:Oral Radiol

Author:

Shakya Kaushlesh Singh,Laddi Amit,Jaiswal Manojkumar^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,Dentistry (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s11282-022-00629-8.pdf

Reference39 articles.

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3. Svystun O, Schropp L, Wenzel A, Spin-Neto R. Sella turcica area and location of point sella in cephalograms acquired with simulated patient head movements. J Contemp Dent Pract. 2021;22(3):207–14.

4. Kantor ML, Norton LA. Normal radiographic anatomy and common anomalies seen in cephalometric films. Am J Orthod Dentofacial Orthop. 1987;91(5):414–26.

5. Subhadra Devi V, Baburao S. Age and sex related morphology and morphometry of sellar region of sphenoid in prenatal and postnatal human cadavers. Int J Res Dev Health. 2013;1(3):141–8.

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