Automated human skull landmarking with 2D Gabor wavelets

Jong, M.A. de; Gül, Atilla; Gijt, J.P. de; Kayser, Manfred; Wolvius, Eppo B.; Böhringer, Stefan

doi:10.1088/1361-6560/aabfa0

Cited by 13 publications

(20 citation statements)

References 19 publications

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“…The cephalometric analysis aims to set landmarks of CT(CBCT) scans which serve as an important factor in the alignment of a skull. These measurements can also be used as surgery planning parameters or pre-and post-surgery comparisons [149,150].…”

Section: Automatization Of Cephalometric Analysismentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

et al. 2021

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Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

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Section: Automatization Of Cephalometric Analysismentioning

confidence: 99%

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

et al. 2021

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“…A promising development is the automatic 3D landmarking using artificial intelligence. Some recent studies have reported errors below 2 mm [22,23], making automatic 3D landmarking a potential alternative. However, as the landmarks are not used for matching but only function as rotation points, the identification of the landmarks has become of little concern.…”

Section: Study Limitationsmentioning

confidence: 99%

Validation of the OrthoGnathicAnalyser 2.0—3D accuracy assessment tool for bimaxillary surgery and genioplasty

et al. 2021

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Orthognathic surgery is a widely performed procedure to correct dentofacial deformities. Virtual treatment planning is an important preparation step. One advantage of the use of virtual treatment planning is the possibility to assess the accuracy of orthognathic surgery. In this study, a tool (OrthoGnathicAnalyser 2.0), which allows for quantification of the accuracy of orthognathic surgery, is presented and validated. In the OrthoGnathicAnalyser 2.0 the accuracy of the osseous chin can now be assessed which was not possible in the earlier version of the OrthoGnathicAnalyser. 30 patients who underwent bimaxillary surgery in combination with a genioplasty were selected from three different centers in the Netherlands. A pre-operative (CB)CT scan, virtual treatment planning and postoperative (CB)CT scan were required for assessing the accuracy of bimaxillary surgery. The preoperative and postoperative (CB)CT scans were aligned using voxel-based matching. Furthermore, voxel-based matching was used to align the pre-operative maxilla, mandible and rami towards their postoperative position whereas surface-based matching was used for aligning the pre-operative chin towards the postoperative position. The alignment resulted in a transformation matrix which contained the achieved translations and rotations. The achieved translations and rotations can be compared to planning values of the virtual treatment plan. To study the reproducibility, two independent observers processed all 30 patients to assess the inter-observer variability. One observer processed the patients twice to assess the intra-observer variability. Both the intra- and inter-observer variability showed high ICC values (> 0.92) and low measurement variations (< 0.673±0.684mm and < 0.654±0.824°). The results of this study show that the OrthoGnathicAnalyser 2.0 has an excellent reproducibility for quantification of skeletal movements between two (CB)CT scans.

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“…Using both sources of information can improve landmarking accuracy (de Jong et al, 2016). It is also possible to combine several landmarking algorithms into a combined method (ensemble) (de Jong et al, 2018b) which can increase flexibility (de Jong et al, 2018a).…”

Section: Landmarkingmentioning

confidence: 99%

“…Replication efforts in genetic studies of facial traits therefore require reproducibility of quantification. Reliability of manual landmarking efforts have been evaluated in some studies (Fagertun et al, 2014; Katina et al, 2015; de Jong et al, 2018a). The agreement between raters was consistently reported to be between 1 and 2 mm across landmarks.…”

Section: Reliability and Heritabilitymentioning

confidence: 99%

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Quantification of Facial Traits

Böhringer

Jong

2019

Front. Genet.

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Measuring facial traits by quantitative means is a prerequisite to investigate epidemiological, clinical, and forensic questions. This measurement process has received intense attention in recent years. We divided this process into the registration of the face, landmarking, morphometric quantification, and dimension reduction. Face registration is the process of standardizing pose and landmarking annotates positions in the face with anatomic description or mathematically defined properties (pseudolandmarks). Morphometric quantification computes pre-specified transformations such as distances. Landmarking: We review face registration methods which are required by some landmarking methods. Although similar, face registration and landmarking are distinct problems. The registration phase can be seen as a pre-processing step and can be combined independently with a landmarking solution. Existing approaches for landmarking differ in their data requirements, modeling approach, and training complexity. In this review, we focus on 3D surface data as captured by commercial surface scanners but also cover methods for 2D facial pictures, when methodology overlaps. We discuss the broad categories of active shape models, template based approaches, recent deep-learning algorithms, and variations thereof such as hybrid algorithms. The type of algorithm chosen depends on the availability of pre-trained models for the data at hand, availability of an appropriate landmark set, accuracy characteristics, and training complexity. Quantification: Landmarking of anatomical landmarks is usually augmented by pseudo-landmarks, i.e., indirectly defined landmarks that densely cover the scan surface. Such a rich data set is not amenable to direct analysis but is reduced in dimensionality for downstream analysis. We review classic dimension reduction techniques used for facial data and face specific measures, such as geometric measurements and manifold learning. Finally, we review symmetry registration and discuss reliability.

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Automated human skull landmarking with 2D Gabor wavelets

Cited by 13 publications

References 19 publications

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Validation of the OrthoGnathicAnalyser 2.0—3D accuracy assessment tool for bimaxillary surgery and genioplasty

Quantification of Facial Traits

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