Fully automatic shape constrained mandible segmentation from cone-beam CT data

Gollmer, Sebastian T.; Buzug, Thorsten M.

doi:10.1109/isbi.2012.6235794

Cited by 33 publications

(30 citation statements)

References 11 publications

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“…In the past decade, some traditional semi-automatic and automatic methods have been developed to segment the mandible in CT scans (Gollmer & Buzug 2012) (Torosdagli et al 2017) (Chuang, Doherty, Adluru, Chung & Vorperian 2017) (Abdi, Kasaei & Mehdizadeh 2015) )(Mannion-Haworth, Bowes, Ashman, Guillard, Brett & Vincent 2015 . A statistical shape model for mandible segmentation was presented by Gollmer and Buzug in 2012 (Gollmer & Buzug 2012). Torosdagli et al proposed a 3D gradient-based fuzzy connectedness algorithm to segment mandibles (Torosdagli et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network

et al. 2019

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Section: Introductionmentioning

confidence: 99%

Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network

et al. 2019

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“…6 Recently, shape information has been utilized for robust segmentation. [7][8][9][10][11][12] Duy et al, 10 proposed a novel statistical shape model for detection and classification of teeth in CBCTs. However, their method may not be able to handle pathological cases, where shapes often change significantly (as shown by examples in the Sec.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation of dental CBCT image with prior-guided sequential random forests

et al. 2015

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Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate 3D models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the image artifacts caused by beam hardening, imaging noise, inhomogeneity, truncation, and maximal intercuspation, it is difficult to segment the CBCT. Methods: In this paper, the authors present a new automatic segmentation method to address these problems. Specifically, the authors first employ a majority voting method to estimate the initial segmentation probability maps of both mandible and maxilla based on multiple aligned expert-segmented CBCT images. These probability maps provide an important prior guidance for CBCT segmentation. The authors then extract both the appearance features from CBCTs and the context features from the initial probability maps to train the first-layer of random forest classifier that can select discriminative features for segmentation. Based on the first-layer of trained classifier, the probability maps are updated, which will be employed to further train the next layer of random forest classifier. By iteratively training the subsequent random forest classifier using both the original CBCT features and the updated segmentation probability maps, a sequence of classifiers can be derived for accurate segmentation of CBCT images. Results: Segmentation results on CBCTs of 30 subjects were both quantitatively and qualitatively validated based on manually labeled ground truth. The average Dice ratios of mandible and maxilla by the authors' method were 0.94 and 0.91, respectively, which are significantly better than the state-of-the-art method based on sparse representation (p-value < 0.001). Conclusions: The authors have developed and validated a novel fully automated method for CBCT segmentation. C

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“…Segmentation time is a crucial step that remains a cumbersome and time‐consuming limitation in the current approach. In the future, automated algorithms may also be capable of solving this task in an automated fashion . However, geometrical accuracy is a paramount requirement for the operation planning and hence for the success of mandibular reconstruction.…”

Section: Discussionmentioning

confidence: 99%

Introduction of an algorithm for planning of autologous fibular transfer in mandibular reconstruction based on individual bone curvatures

Raith

Rauen

Möhlhenrich

et al. 2018

Robotics Computer Surgery

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Fully automatic shape constrained mandible segmentation from cone-beam CT data

Cited by 33 publications

References 11 publications

Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network

Automatic segmentation of the mandible from computed tomography scans for 3D virtual surgical planning using the convolutional neural network

Automated segmentation of dental CBCT image with prior-guided sequential random forests

Introduction of an algorithm for planning of autologous fibular transfer in mandibular reconstruction based on individual bone curvatures

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