U-Net-based image segmentation of the whole heart and four chambers on pediatric X-ray computed tomography-Reference-Cited by-同舟云学术

U-Net-based image segmentation of the whole heart and four chambers on pediatric X-ray computed tomography

Published:2022-05-07 Issue:2 Volume:15 Page:156-169
ISSN:1865-0333
Container-title:Radiological Physics and Technology
language:en
Short-container-title:Radiol Phys Technol

Author:

Yoshida Akifumi^ORCID,Kondo Yohan,Yoshimura Norihiko,Kuramoto Tatsuya,Hasegawa Akira,Kanazawa Tsutomu

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,Physical Therapy, Sports Therapy and Rehabilitation,General Medicine,Radiation

Link

https://link.springer.com/content/pdf/10.1007/s12194-022-00657-3.pdf

Reference19 articles.

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2. Han BK, Rigsby CK, Hlavacek A, Leipsic J, Nicol ED, Siegel MJ, Bardo D, Abbara S, Ghoshhajra B, Lesser JR, Raman S, Crean AM, Society of Cardiovascular Computed Tomography, Society of Pediatric Radiology, North American Society of Cardiac Imaging. Computed tomography imaging in patients with congenital heart disease Part I: rationale and utility. an expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT): endorsed by the Society of Pediatric Radiology (SPR) and the North American Society of Cardiac Imaging (NASCI). J Cardiovasc Comput Tomogr. 2015;9:475–92. https://doi.org/10.1016/j.jcct.2015.07.004.

3. Yu L, Cheng JZ, Dou Q, Yang X, Chen H, Qin J, Heng PA. Automatic 3D cardiovascular MR segmentation with densely-connected volumetric ConvNets. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins D, Duchesne S, editors. Medical image computing and computer-assisted intervention—MICCAI 2017. Lecture notes in computer science; 2017. https://doi.org/10.1007/978-3-319-66185-8_33.

4. Li J, Zhang R, Shi L, Wang D. Automatic whole-heart segmentation in congenital heart disease using deeply-supervised 3D FCN. In: Zuluaga M, Bhatia K, Kainz B, Moghari M, Pace D, editors. Reconstruction, segmentation, and analysis of medical images. RAMBO 2016, HVSMR 2016. Lecture notes in computer science; 2017. https://doi.org/10.1007/978-3-319-52280-7_11.

5. Li C, Tong Q, Liao X, Si W, Chen S, Wang Q, Yuan Z. APCP-NET: aggregated parallel cross-scale pyramid network for CMR segmentation. In: 16th IEEE International Symposium on Biomedical Imaging, ISBI, Venice, 2019:784–8.

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