TY - JOUR
T1 - A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery
AU - Minnema, Jordi
AU - Ernst, Anne
AU - van Eijnatten, Maureen
AU - Pauwels, Ruben
AU - Forouzanfar, Tymour
AU - Batenburg, Kees Joost
AU - Wolff, Jan
N1 - Funding Information: We would like to thank Linda J. Schoonmade (Department of Medical Library, Vrije Universiteit Amsterdam) for defining adequate search terms and inclusion criteria. MvE and KJB acknowledge financial support from the Netherlands Organisation for Scientific Research (NWO), project number 639.073.506. In addition, MvE, and KJB acknowledge financial support by Holland High Tech through the PPP allowance for research and development in the HTSM topsector. RP is supported by the European Union Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant agreement (number 754513) and by Aarhus University Research Foundation (AIAS-COFUND). Publisher Copyright: © 2022 The Authors. Published by the British Institute of Radiology.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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.
AB - 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.
KW - CT image reconstruction
KW - bone segmentation
KW - computer-assisted surgery
KW - neural networks
KW - surgical planning
UR - http://www.scopus.com/inward/record.url?scp=85138489567&partnerID=8YFLogxK
U2 - https://doi.org/10.1259/dmfr.20210437
DO - https://doi.org/10.1259/dmfr.20210437
M3 - Review article
C2 - 35532946
SN - 0250-832X
VL - 51
SP - 20210437
JO - Dentomaxillofacial radiology
JF - Dentomaxillofacial radiology
IS - 7
M1 - 20210437
ER -