Introducing molaR: a New R Package for Quantitative Topographic Analysis of Teeth (and Other Topographic Surfaces)

Abstract

Researchers studying mammalian dentitions from functional and adaptive perspectives increasingly have moved towards using dental topography measures that can be estimated from 3D surface scans, which do not require identification of specific homologous landmarks. Here we present molaR, a new R package designed to assist researchers in calculating four commonly used topographic measures: Dirichlet Normal Energy (DNE), Relief Index (RFI), Orientation Patch Count (OPC), and Orientation Patch Count Rotated (OPCR) from surface scans of teeth, enabling a unified application of these informative new metrics. In addition to providing topographic measuring tools, molaR has complimentary plotting functions enabling highly customizable visualization of results. This article gives a detailed description of the DNE measure, walks researchers through installing, operating, and troubleshooting molaR and its functions, and gives an example of a simple comparison that measured teeth of the primates Alouatta and Pithecia in molaR and other available software packages. molaR is a free and open source software extension, which can be found at the

Smooth operator: The effects of different 3D mesh retriangulation protocols on the computation of Dirichlet normal energy

“…First, published DNE analysis has only been performed by a narrow set of researchers connected to a small number of research groups including Boyer and Winchester (e.g., Bunn et al, ; Ledogar, Winchester, St. Clair, & Boyer, ; Prufrock, Boyer, & Silcox, ; Winchester, ; Winchester et al, ) and Pampush and Kay (e.g., Pampush et al, ; this study) suggesting that the method has remained somewhat inaccessible. Second, every study utilizing DNE has used a slightly different protocol to produce and analyze 3D meshes, raising concerns about the comparability and repeatability of results (Pampush et al, ). Development of a generalized protocol to prepare 3D meshes for DNE analysis is an essential step forward allowing for greater methodological transparency and improved clarity of results and their interpretations.…”

The case of the grass‐eating suids in the Plio‐Pleistocene Turkana Basin: 3D dental topography in relation to diet in extant and fossil pigs

“…Other studies have used 50 data rows for one tooth (Smits & Evans, ; Winchester et al, ). For “molaR,” which uses the 3D‐meshes instead of raster files, the face count of the mesh was reduced to 10,000 and Laplacian smoothing (three steps) was done in MeshLab (Boyer, ; Bunn et al, ; Pampush et al, ; Spradley, Pampush, Morse, & Kay, ). JMP Pro 13 (SAS Institute Inc., Cary, NC) was used to perform the statistical analyses.…”

“…In this study R-package "molaR" (Pampush et al, 2016) was used for DNE, OPC, and MSS calculations (but not for relief index, because "molaR" uses a relief index function, which is designed for whole tooth [Boyer, 2008]), "rgdal" package for MSS and OPC, ArcGIS 10.3 (Esri, Redlands, California) for sharpness (natural breaks method classification) and relief index, F I G U R E 1 Cropping of the third molars was done immediately after the second cusp pair, going from the mesial toward the distal direction and SurferManipulator (Evans, 2008) for angularity, MSS and OPC analyses. R-package "rgdal" was used to rasterize the polygon data (shapefile) and convert the raster files to 0.05 mm resolution, or resize them to 50 and 150 data rows.…”

“…Geographic information systems‐tools consider tooth surfaces analogous to three‐dimensional landscapes (Hunter & Jernvall, ; Reed, ; Ungar & Williamson, ; Zucotti, Williamson, Limp, & Ungar, ), allowing measurements from the whole occlusal surface, making it independent of landmarks, unlike geometric morphometrics (Ungar & Williamson, ; Evans, ). Most GIS‐based dental topography studies to date have been focused on primates (Boyer, ; Bunn & Ungar, ; Dennis, Ungar, Teaford, & Glander, ; M'Kirera & Ungar, ; Pampush et al, ; Ungar, Healy, Karme, Teaford, & Fortelius, ; Ungar & M'Kirera, ; Winchester et al, ; Yamashita et al, ). However, orientation patch count (OPC, and orientation patch count rotated [OPCR]) analysis, which is based on geographical orientation of patches on the tooth surface, has also been applied to other animal groups (Evans & Janis, ; Evans & Jernvall, ; Evans, Wilson, Fortelius, & Jernvall, ; Santana, Strait, & Dumont, ; Smits & Evans, ; Wilson et al, ).…”

The Geometry of Synchronization Problems and Learning Group Actions

“…Algorithms that automatically align a collection of anatomical shapes in a globally consistent manner can also be viewed as primitive approaches for solving E (3)-synchronization problems; see e.g. [124,24,120,73].…”