Cranial Defect Reconstruction Using Cascaded CNN with Alignment
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU138873" target="_blank" >RIV/00216305:26230/20:PU138873 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-64327-0_7" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-64327-0_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-64327-0_7" target="_blank" >10.1007/978-3-030-64327-0_7</a>
Alternative languages
Result language
angličtina
Original language name
Cranial Defect Reconstruction Using Cascaded CNN with Alignment
Original language description
Designing a patient-specific cranial implant usually requires reconstructing the defective part of the skull using computer-aided design software, which is a tedious and time-demanding task. This lead to some recent advances in the field of automatic skull reconstruction with use of methods based on shape analysis or deep learning. The AutoImplant Challenge aims at providing a public platform for benchmarking skull reconstruction methods. The BUT submission to this challenge is based on skull alignment using landmark detection followed by a cascade of low-resolution and high-resolution reconstruction convolutional neural network. We demonstrate that the proposed method successfully reconstructs every skull in the standard test dataset and outperforms the baseline method in both overlap and distance metrics, achieving 0.920 DSC and 4.137 mm HD.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Towards the Automatization of Cranial Implant Design in Cranioplasty
ISBN
978-3-030-64327-0
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
56-64
Publisher name
Springer Nature Switzerland AG
Place of publication
Lima
Event location
Lima
Event date
Oct 8, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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