A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery Article Swipe
Jordi Minnema
,
Anne Ernst
,
Maureen van Eijnatten
,
Ruben Pauwels
,
Tymour Forouzanfar
,
Kees Joost Batenburg
,
Jan Wolff
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1259/dmfr.20210437
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1259/dmfr.20210437
Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popular treatment modality in maxillofacial surgery. The current maxillofacial CAS consists of three main steps: (1) CT image reconstruction, (2) bone segmentation, and (3) surgical planning. However, each of these three steps can introduce errors that can heavily affect the treatment outcome. As a consequence, tedious and time-consuming manual post-processing is often necessary to ensure that each step is performed adequately. One way to overcome this issue is by developing and implementing neural networks (NNs) within the maxillofacial CAS workflow. These learning algorithms can be trained to perform specific tasks without the need for explicitly defined rules. In recent years, an extremely large number of novel NN approaches have been proposed for a wide variety of applications, which makes it a difficult task to keep up with all relevant developments. This study therefore aimed to summarize and review all relevant NN approaches applied for CT image reconstruction, bone segmentation, and surgical planning. After full text screening, 76 publications were identified: 32 focusing on CT image reconstruction, 33 focusing on bone segmentation and 11 focusing on surgical planning. Generally, convolutional NNs were most widely used in the identified studies, although the multilayer perceptron was most commonly applied in surgical planning tasks. Moreover, the drawbacks of current approaches and promising research avenues are discussed.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1259/dmfr.20210437
- OA Status
- green
- Cited By
- 37
- References
- 96
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4229448187
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4229448187Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1259/dmfr.20210437Digital Object Identifier
- Title
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A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgeryWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-09Full publication date if available
- Authors
-
Jordi Minnema, Anne Ernst, Maureen van Eijnatten, Ruben Pauwels, Tymour Forouzanfar, Kees Joost Batenburg, Jan WolffList of authors in order
- Landing page
-
https://doi.org/10.1259/dmfr.20210437Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://research.tue.nl/nl/publications/e68b48f8-d76c-4f6d-91a5-79759e82252fDirect OA link when available
- Concepts
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Workflow, Surgical planning, Segmentation, Computer science, Convolutional neural network, Artificial intelligence, Deep learning, Radiation treatment planning, Variety (cybernetics), Medical physics, Machine learning, Medicine, Radiology, Radiation therapy, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
37Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 13, 2024: 12, 2023: 11, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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96Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2022-05-09 |
| publication_year | 2022 |
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