Robust and fully automated segmentation of mandible from CT scans

Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. Furthermore, connections of teeth to mandible and mandible to remaining parts of the skull make it extremely difficult to identify mandible boundary automatically. This study addresses these challenges by proposing a novel framework where we define the segmentation as two complementary tasks: recognition and delineation. For recognition, we use random forest regression to localize mandible in 3D. For delineation, we propose to use 3D gradient-based fuzzy connectedness (FC) image segmentation algorithm, operating on the recognized mandible sub-volume. Despite heavy CT artifacts and dental fillings, consisting half of the CT image data in our experiments, we have achieved highly accurate detection and delineation results. Specifically, detection accuracy more than 96% (measured by union of intersection (UoI)), the delineation accuracy of 91% (measured by dice similarity coefficient), and less than 1 mm in shape mismatch (Hausdorff Distance) were found.Comment: 4 pages, 5 figures, IEEE International Symposium on Biomedical Imaging (ISBI) 201

Relational Reasoning Network (RRN) for Anatomical Landmarking

Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for craniomaxillofacial (CMF) bones. Available methods require segmentation of the object of interest for precise landmarking. Unlike those, our purpose in this study is to perform anatomical landmarking using the inherent relation of CMF bones without explicitly segmenting them. We propose a new deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations of the landmarks. Specifically, we are interested in learning landmarks in CMF region: mandible, maxilla, and nasal bones. The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units and without the need for segmentation. For a given a few landmarks as input, the proposed system accurately and efficiently localizes the remaining landmarks on the aforementioned bones. For a comprehensive evaluation of RRN, we used cone-beam computed tomography (CBCT) scans of 250 patients. The proposed system identifies the landmark locations very accurately even when there are severe pathologies or deformations in the bones. The proposed RRN has also revealed unique relationships among the landmarks that help us infer several reasoning about informativeness of the landmark points. RRN is invariant to order of landmarks and it allowed us to discover the optimal configurations (number and location) for landmarks to be localized within the object of interest (mandible) or nearby objects (maxilla and nasal). To the best of our knowledge, this is the first of its kind algorithm finding anatomical relations of the objects using deep learning.Comment: 10 pages, 6 Figures, 3 Table

Craniofacial Analysis May Indicate Co-Occurrence of Skeletal Malocclusions and Associated Risks in Development of Cleft Lip and Palate

Non-syndromic orofacial clefts encompass a range of morphological changes affecting the oral cavity and the craniofacial skeleton, of which the genetic and epigenetic etiologic factors remain largely unknown. The objective of this study is to explore the contribution of underlying dentofacial deformities (also known as skeletal malocclusions) in the craniofacial morphology of non-syndromic cleft lip and palate patients (nsCLP). For that purpose, geometric morphometric analysis was performed using full skull cone beam computed tomography (CBCT) images of patients with nsCLP (n = 30), normocephalic controls (n = 60), as well as to sex- and ethnicity- matched patients with an equivalent dentofacial deformity (n = 30). Our outcome measures were shape differences among the groups quantified via principal component analysis and associated principal component loadings, as well as mean shape differences quantified via a Procrustes distance among groups. According to our results, despite the shape differences among all three groups, the nsCLP group shares many morphological similarities in the maxilla and mandible with the dentofacial deformity group. Therefore, the dentoskeletal phenotype in nsCLP could be the result of the cleft and the coexisting dentofacial deformity and not simply the impact of the cleft

Genetics of murine craniofacial morphology: diallel analysis of the eight founders of the Collaborative Cross

Using eight inbred founder strains of the mouse Collaborative Cross (CC) project and their reciprocal F1 hybrids, we quantified variation in craniofacial morphology across mouse strains, explored genetic contributions to craniofacial variation that distinguish the founder strains, and tested whether specific or summary measures of craniofacial shape display stronger additive genetic contributions. This study thus provides critical information about phenotypic diversity among CC founder strains and about the genetic contributions to this phenotypic diversity, which is relevant to understanding the basis of variation in standard laboratory strains and natural populations. Craniofacial shape was quantified as a series of size-adjusted linear dimensions (RDs) and by principal components (PC) analysis of morphological landmarks captured from computed tomography images from 62 out of the 64 reciprocal crosses of the CC founder strains. We first identified aspects of skull morphology that vary between these phenotypically ‘normal’ founder strains and that are defining characteristics of these strains. We estimated the contributions of additive and various non-additive genetic factors to phenotypic variation using diallel analyses of a subset of these strongly differing RDs and the first 8 PCs of skull shape variation. We find little difference in the genetic contributions to RD measures and PC scores, suggesting fundamental similarities in the magnitude of genetic contributions to both specific and summary measures of craniofacial phenotypes. Our results indicate that there are stronger additive genetic effects associated with defining phenotypic characteristics of specific founder strains, suggesting these distinguishing measures are good candidates for use in genotype-phenotype association studies of CC mice. Our results add significantly to understanding of genotype-phenotype associations in the skull, which serve as a foundation for modeling the origins of medically and evolutionarily relevant variation

Fine Tuning of Craniofacial Morphology by Distant-Acting Enhancers

The shape of the human face and skull is largely genetically determined, but the genetic drivers of craniofacial morphology remain poorly understood. Here we used a combination of epigenomic profiling, in vivo characterization of candidate enhancer sequences in transgenic mice, and targeted deletion experiments to examine the role of distant-acting enhancers in craniofacial development. We identified complex regulatory landscapes, consisting of enhancers that drive a remarkable spatial complexity of developmental expression patterns. Deletion of individual craniofacial enhancers from the mouse genome resulted in significant alterations of craniofacial shape, demonstrating their functional importance in defining face and skull morphology. These results demonstrate that enhancers play a pervasive role in mammalian craniofacial development and suggest that enhancer sequence variation contributes to human facial morphology

Using admixture mapping to identify genetic linkages with variation in human facial shape

Liberton D.K., Claes P., Beleza S., Barsh G., Tang H., Shriver M.D., ''Using admixture mapping to identify genetic linkages with variation in human facial shape'', American journal of physical anthropology, pp. 180, 2013 (82nd annual meeting of the American Association of Physical Anthropologists (AAPA), April 9-13, 2013, Knoxville, Tennessee, USA).status: publishe

Relational reasoning network for anatomical landmarking

PURPOSE: We perform anatomical landmarking for craniomaxillofacial (CMF) bones without explicitly segmenting them. Toward this, we propose a simple, yet efficient, deep network architecture, called relational reasoning network (RRN), to accurately learn the local and the global relations among the landmarks in CMF bones; specifically, mandible, maxilla, and nasal bones. APPROACH: The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units. For a given few landmarks as input, RRN treats the landmarking process similar to a data imputation problem where predicted landmarks are considered missing. RESULTS: We applied RRN to cone-beam computed tomography scans obtained from 250 patients. With a fourfold cross-validation technique, we obtained an average root mean squared error of per landmark. Our proposed RRN has revealed unique relationships among the landmarks that help us in inferring informativeness of the landmark points. The proposed system identifies the missing landmark locations accurately even when severe pathology or deformations are present in the bones. CONCLUSIONS: Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for CMF surgeries. Achieving this goal without the need for explicit bone segmentation addresses a major limitation of segmentation-based approaches, where segmentation failure (as often is the case in bones with severe pathology or deformation) could easily lead to incorrect landmarking. To the best of our knowledge, this is the first-of-its-kind algorithm finding anatomical relations of the objects using deep learning

Using automated high density quasi-landmarks to test for associations between normal facial feature variation, genetic ancestry and candidate gene variation in Cape Verdeans

Liberton D.K., Claes P., Mcevoy B., Beleza S., Barsh G., Tang H., Absher D., Shriver M.D., ''Using automated high density quasi-landmarks to test for associations between normal facial feature variation, genetic ancestry and candidate gene variation in Cape Verdeans'', American journal of physical anthropology, pp. 198, 2011 (80th annual meeting of the American Association of Physical Anthropologists (AAPA), April 12-16, 2011, Minneapolis, Minnesota, USA).status: publishe