ALPACA: a fast and accurate approach for automated landmarking of three-dimensional biological structures

Abstract

Landmark-based geometric morphometrics has emerged as an essential discipline for the quantitative analysis of size and shape in ecology and evolution. With the ever-increasing density of digitized landmarks, the possible development of a fully automated method of landmark placement has attracted considerable attention. Despite the recent progress in image registration techniques, which could provide a pathway to automation, three-dimensional morphometric data is still mainly gathered by trained experts. For the most part, the large infrastructure requirements necessary to perform image-based registration, together with its system-specificity and its overall speed have prevented wide dissemination.Here, we propose and implement a general and lightweight point cloud-based approach to automatically collect highdimensional landmark data in 3D surfaces (Automated Landmarking through Point cloud Alignment and Correspondence Analysis). Our framework possesses several advantages compared with image-based approaches. First, it presents comparable landmarking accuracy, despite relying on a single, random reference specimen and much sparser sampling of the structure’s surface. Second, it is performant such that it can be efficiently run on consumer-grade personal computers. Finally, it is general and can be applied to any biological structure of interest, regardless of whether anatomical atlases are available.Our validation procedures indicate that the method is capable of recovering multivariate patterns of morphological variation that are largely indistinguishable from those obtained by manual digitization, indicating that the use of an automated landmarking approach should not result in different conclusions regarding the nature of multivariate patterns of morphological variation.The proposed point cloud-based approach has the potential to increase the scale and reproducibility of morphometrics research. To allow ALPACA to be used out-of-the-box by users with no prior programming experience, we implemented it as a module as part of the SlicerMorph project. SlicerMorph is an extension that enables geometric morphometrics data collection and 3D specimen analysis within the open-source 3D Slicer biomedical visualization ecosystem. We expect that convenient access to this platform will make ALPACA broadly applicable within ecology and evolution.

Computational anatomy and geometric shape analysis enables analysis of complex craniofacial phenotypes in zebrafish

“…Both of these structures are highly prone to plastic post-mortem deformation due to handling and preservation, as such they represent confounding non-biological variation. To transfer the pseudo-landmark points from the atlas to all other models in the study, we used the ALPACA module in the SlicerMorph extension of 3D Slicer, which uses linear and deformable point cloud registration (Porto et al, 2020). We used the default settings and skipped the scaling option to transfer pseudolandmarks from the atlas to all meshes in our sample (Figures 1, S2).…”

Computer vision, machine learning, and the promise of phenomics in ecology and evolutionary biology

“…Despite its popularity, landmark data is still collected mainly through manual annotation, a process which represents a significant bottleneck for phenomic studies. However, machine-learning-based CV can be used to accurately automate landmark data collection in morphometric studies not only in 2D (McPeek et al, 2008;Porto and Voje, 2020b) , but also in 3D (Porto et al, 2020) .…”

SlicerMorph: An open and extensible platform to retrieve, visualize and analyze 3D morphology

“…An optimal set of parameters that gives the best correspondence can be investigated (and outcome can be visualized) in pairwise alignment mode, and then applied to a number of 3D models in batch mode. A paper detailing the specifics of the method and its performance is currently being reviewed, and a preprint version is available on bioRxiv [39]. Below we provide a brief outline of the method and refer the reader to the preprint for technical details of the implementation.…”

“…In most practical applications, the main parameters that will need to be tuned are the deformable registration parameters, as those can vary considerably across structures and/or species. A preprint detailing the specifics on the ALPACA implementation and its performance on a number of mammalian skulls can be found in bioRxiv [39].…”