Automated Landmarking via Multiple Templates

Geometric morphometrics based on landmark data has been increasingly used in biomedical and biological researchers for quantifying complex phenotypes. However, manual landmarking can be laborious and subject to intra and interobserver errors. This has motivated the development of automated landmarking methods. We have recently introduced ALPACA (Automated Landmarking through Point cloud Alignment and Correspondence), a fast method to automatically annotate landmarks via use of a landmark template as part of the SlicerMorph toolkit. Yet, using a single template may not consistently perform well for large study samples, especially when the sample consists of specimen with highly variable morphology, as it is common evolutionary studies. In this study, we introduce a variation on our ALPACA pipeline that supports multiple specimen templates, which we call MALPACA. We show that MALPACA outperforms ALPACA consistently by testing on two different datasets. We also introduce a method of choosing the templates that can be used in conjunction with MALPACA, when no prior information is available. This K-means method uses an approximation of the total morphological variation in the dataset to suggest samples within the population to be used as landmark templates. While we advise investigators to pay careful attention to the template selection process in any of the template-based automated landmarking approaches, our analyses show that the introduced K-means based method of templates selection is better than randomly choosing the templates. In summary, MALPACA can accommodate larger morphological disparity commonly found in evolutionary studies with performance comparable to human observer.

Quantifying asymmetry in non-symmetrical morphologies, with an example from Cetacea

Three-dimensional measurements of morphology are key to gaining an understanding of a species' biology and to answering subsequent questions regarding the processes of ecology (or palaeoecology), function, and evolution. However, the collection of morphometric data is often focused on methods designed to produce data on bilaterally symmetric morphologies which may mischaracterise asymmetric structures. Using 3D landmark and curve data on 3D surface meshes of specimens, we present a method for first quantifying the level of asymmetry in a specimen and second, accurately capturing the morphology of asymmetric specimens for further geometric analyses. We provide an example of the process from initial landmark placement, including details on how to place landmarks to quantify the level of asymmetry, and then on how to use this information to accurately capture the morphology of asymmetric morphologies or structures. We use toothed whales (odontocetes) as a case study and include examples of the consequences of mirroring landmarks and curves, a method commonly used in bilaterally symmetrical specimens, on asymmetric specimens. We conclude by presenting a step-by-step method to collecting 3D landmark data on asymmetric specimens. Additionally, we provide code for placing landmarks and curves on asymmetric specimens in a manner designed to both save time and ultimately accurately quantify morphology. This method can be used as a first crucial step in morphometric analyses of any biological specimens by assessing levels of asymmetry and then if required, accurately quantifying this asymmetry. The latter not only saves the researcher time, but also accurately represents the morphology of asymmetric structures.

An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data

Background To estimate median liver iron concentration (LIC) calculated from magnetic resonance imaging, excluded vessels of the liver parenchyma region were defined manually. Previous works proposed the automated method for excluding vessels from the liver region. However, only user-defined liver region remained a manual process. Therefore, this work aimed to develop an automated liver region segmentation technique to automate the whole process of median LIC calculation. Methods 553 MR examinations from 471 thalassemia major patients were used in this study. LIC maps (in mg/g dry weight) were calculated and used as the input of segmentation procedures. Anatomical landmark data were detected and used to restrict ROI. After that, the liver region was segmented using fuzzy c-means clustering and reduced segmentation errors by morphological processes. According to the clinical application, erosion with a suitable size of the structuring element was applied to reduce the segmented liver region to avoid uncertainty around the edge of the liver. The segmentation results were evaluated by comparing with manual segmentation performed by a board-certified radiologist. Results The proposed method was able to produce a good grade output in approximately 81% of all data. Approximately 11% of all data required an easy modification step. The rest of the output, approximately 8%, was an unsuccessful grade and required manual intervention by a user. For the evaluation matrices, percent dice similarity coefficient (%DSC) was in the range 86–92, percent Jaccard index (%JC) was 78–86, and Hausdorff distance (H) was 14–28 mm, respectively. In this study, percent false positive (%FP) and percent false negative (%FN) were applied to evaluate under- and over-segmentation that other evaluation matrices could not handle. The average of operation times could be reduced from 10 s per case using traditional method, to 1.5 s per case using our proposed method. Conclusion The experimental results showed that the proposed method provided an effective automated liver segmentation technique, which can be applied clinically for automated median LIC calculation in thalassemia major patients.

A New Method for Landmark-Based Studies of the Dynamic Stability of Growth, with Implications for Evolutionary Analyses

A matrix manipulation new to the quantitative study of develomental stability reveals unexpected morphometric patterns in a classic data set of landmark-based calvarial growth. There are implications for evolutionary studies. Among organismal biology’s fundamental postulates is the assumption that most aspects of any higher animal’s growth trajectories are dynamically stable, resilient against the types of small but functionally pertinent transient perturbations that may have originated in genotype, morphogenesis, or ecophenotypy. We need an operationalization of this axiom for landmark data sets arising from longitudinal data designs. The present paper introduces a multivariate approach toward that goal: a method for identification and interpretation of patterns of dynamical stability in longitudinally collected landmark data. The new method is based in an application of eigenanalysis unfamiliar to most organismal biologists: analysis of a covariance matrix of Boas coordinates (Procrustes coordinates without the size standardization) against their changes over time. These eigenanalyses may yield complex eigenvalues and eigenvectors (terms involving $$i=\sqrt{-1}$$ i = - 1 ); the paper carefully explains how these are to be scattered, gridded, and interpreted by their real and imaginary canonical vectors. For the Vilmann neurocranial octagons, the classic morphometric data set used as the running example here, there result new empirical findings that offer a pattern analysis of the ways perturbations of growth are attenuated or otherwise modified over the course of developmental time. The main finding, dominance of a generalized version of dynamical stability (negative autoregressions, as announced by the negative real parts of their eigenvalues, often combined with shearing and rotation in a helpful canonical plane), is surprising in its strength and consistency. A closing discussion explores some implications of this novel pattern analysis of growth regulation. It differs in many respects from the usual way covariance matrices are wielded in geometric morphometrics, differences relevant to a variety of study designs for comparisons of development across species.

Pooled analyses of immune-related adverse events (irAEs) and efficacy from the phase 3 trials IMpower130, IMpower132, and IMpower150.

9002 Background: PD-L1/PD-1 inhibitors have transformed the treatment (tx) of advanced NSCLC. Evidence suggests that the occurrence of irAEs with these agents may predict improved outcomes in cancers such as NSCLC. Atezolizumab (atezo; anti–PD-L1) has shown efficacy and tolerability in NSCLC and is currently approved in the 1L and 2L+ settings. The Ph 3 IMpower130, IMpower132 and IMpower150 trials evaluated atezo + chemo ± bevacizumab (bev) as 1L tx of NSCLC. We explore the association between irAEs and efficacy in these trials. Methods: Each trial enrolled tx-naive patients (pts) with nonsquamous stage IV NSCLC. Pts were randomized to: carboplatin (carbo) + nab-paclitaxel alone or with atezo in IMpower130; carbo or cisplatin alone or with atezo in IMpower132; atezo (A) + bev (B) + carbo + paclitaxel (CP), ACP or BCP in IMpower150. Data were pooled (data cutoffs: Mar 15 2018 [IMpower130]; May 22 2018 [IMpower132]; Sep 13 2019 [IMpower150]) and analyzed by tx (atezo-containing vs control) and irAE status. A time-dependent Cox model and landmark analyses at 1, 3, 6 and 12 mo were used to control for immortal bias. Study protocols required atezo tx interruption/discontinuation for grade (Gr) ≥3 irAEs. Results: 2503 pts were included in the analysis (atezo, n = 1577; control, n = 926). In both arms, baseline characteristics were generally balanced between pts with irAEs (atezo, n = 753; control, n = 289) and without irAEs (atezo, n = 824; control, n = 637). Any-Gr irAEs occurred in 48% (atezo) and 32% (control) of pts; Gr 3-5 irAEs occurred in 11% (atezo) and 5% (control). The most common irAEs (atezo vs control) were rash (28% vs 18%), hepatitis (lab abnormalities; 15% vs 10%) and hypothyroidism (12% vs 4%). Median time to onset of first irAE was 1.7 (atezo) vs 1.4 mo (control). OS HRs (95% CI) from the time-dependent Cox model between pts with vs without irAEs were 0.69 (0.60, 0.78) in the atezo arm and 0.82 (0.68, 0.99) in the control arm; after excluding rash (perceived as the least specific irAE), OS HRs (95% CI) were 0.75 (0.65, 0.87) and 0.90 (0.71, 1.12), respectively. OS landmark data are in the Table. Conclusions: In this exploratory pooled analysis, pts with irAEs had longer OS vs pts without irAEs in the atezo-containing and control arms per the time-dependent Cox model and landmark analyses; this trend remained for the atezo arm after excluding rash. Landmark analyses suggest that in the atezo arm, pts with Gr 1/2 irAEs had the longest OS and pts with Gr ≥3 irAEs had the shortest OS, potentially due to tx interruption/discontinuation. Clinical trial information: NCT02367781; NCT02657434; NCT02366143. [Table: see text]

Utilizing Scp for the analysis and replication of single-cell proteomics data

IntroductionMass spectrometry-based proteomics is actively embracing quantitative, single cell-level analyses. Indeed, recent advances in sample preparation and mass spectrometry (MS) have enabled the emergence of quantitative MS-based single-cell proteomics (SCP). While exciting and promising, SCP still has many rough edges. The current analysis workflows are custom and build from scratch. The field is therefore craving for standardized software that promotes principled and reproducible SCP data analyses.Areas coveredThis special report represents the first step toward the formalization of standard SCP data analysis. Scp, the software that accompanies this work can successfully reproduces one of the landmark data in the field of SCP. We created a repository containing the reproduction workflow with comprehensive documentation in order to favor further dissemination and improvement of SCP data analyses.Expert opinionReproducing SCP data analyses uncovers important challenges in SCP data analysis. We describe two such challenges in detail: batch correction and data missingness. We provide the current state-of-the-art and illustrate the associated limitations. We also highlights the intimate dependence that exists between batch effects and data missingness and provides future tracks for dealing with these exciting challenges.1Article highlightsSingle-cell proteomics (SCP) is emerging thanks to several recent technological advances, but further progress is lagging due to principled and systematic data analysis.This work offers a standardized solution for the processing of SCP data demonstrated by the reproduction of a landmark SCP work.Two important challenges remain: batch effects and data missingness. Furthermore, these challenges are not independent and therefore need to be modeled simultaneously.

De novo homology assessment from landmark data: A workflow to identify and track segmented structures in plant time series images

Assessing the phenotypes underlying plant growth and development is integral to exploring the development, genetics, and evolution of morphology and plays an essential role in agronomic and basic research studies. Although various automated or semi-automated phenomic approaches have recently been developed, tools assessing differential growth of plant organs remains a key topic of interest, but one which is often difficult to analyze due to the requirements of segmenting and annotating specific structures or positions in the plant body in time-series data. To address this gap, we have developed a generalized workflow linking our previously published function, acute, with a companion function, homology, in the PlantCV environment. The homology function uses a generalized strategy of dimensionality reduction via starscape followed by hierarchical clustering through constella to identify ‘constellations’ of segments in eigenspace that represent the same landmark in consecutive images of a time-series. We devised a quality control function, constellaQC, that can test the accuracy of the clustering approach, and we use it to show that the approach accurately clustered the pseudo-landmarks derived from acute, although with several sources of error. We discuss the reasons for and consequences of these errors in automated workflows, and suggest how to develop these functions so that they can easily be repurposed for other phenomics datasets that may vary in dimensional complexity.

Centric Allometry: Studying Growth Using Landmark Data

The geometric morphometric (GMM) construction of Procrustes shape coordinates from a data set of homologous landmark configurations puts exact algebraic constraints on position, orientation, and geometric scale. While position as digitized is not ordinarily a biologically meaningful quantity, and orientation is relevant mainly when some organismal function interacts with a Cartesian positional gradient such as horizontality, size per se is a crucially important biometric concept, especially in contexts like growth, biomechanics, or bioenergetics. “Normalizing” or “standardizing” size (usually by dividing the square root of the summed squared distances from the centroid out of all the Cartesian coordinates specimen by specimen), while associated with the elegant symmetries of the Mardia–Dryden distribution in shape space, nevertheless can substantially impeach the validity of any organismal inferences that ensue. This paper adapts two variants of standard morphometric least-squares, principal components and uniform strains, to circumvent size standardization while still accommodating an analytic toolkit for studies of differential growth that supports landmark-by-landmark graphics and thin-plate splines. Standardization of position and orientation but not size yields the coordinates Franz Boas first discussed in 1905. In studies of growth, a first principal component of these coordinates often appears to involve most landmarks shifting almost directly away from their centroid, hence the proposed model’s name, “centric allometry.” There is also a joint standardization of shear and dilation resulting in a variant of standard GMM’s “nonaffine shape coordinates” where scale information is subsumed in the affine term. Studies of growth allometry should go better in the Boas system than in the Procrustes shape space that is the current conventional workbench for GMM analyses. I demonstrate two examples of this revised approach (one developmental, one phylogenetic) that retrieve all the findings of a conventional shape-space-based approach while focusing much more closely on the phenomenon of allometric growth per se. A three-part Appendix provides an overview of the algebra, highlighting both similarities to the Procrustes approach and contrasts with it.

On the Challenge to Correctly Identify Rasboras (Teleostei: Cyprinidae: Danioninae) Inhabiting the Mesangat Wetlands, East Kalimantan, Indonesia

Within the subfamily Danioninae, rasborine cyprinids are known as a ‘catch-all’ group, diagnosed by only a few characteristics. Most species closely resemble each other in morphology. Species identification is therefore often challenging. In this study, we attempted to determine the number of rasborine species occurring in samples from the Mesangat wetlands in East Kalimantan, Indonesia, by using different approaches. Morphological identification resulted in the distinction of five species (Trigonopoma sp., Rasbora cf. hubbsi Brittan, 1954, R. rutteni Weber and de Beaufort, 1916, R. trilineata Steindachner, 1870, and R. vaillantii, Popta 1905). However, genetic species delimitation methods (Poisson tree processes (PTP) and multi-rate PTP (mPTP)) based on DNA barcodes and principal component analysis (PCA) based on homologous geometric morphometric landmarks, revealed a single cluster for Trigonopoma sp. and R. trilineata, respectively, whereas the remaining traditionally identified species were distinguished neither by DNA barcodes nor by the morphometry approach. A k-mean clustering based on the homologous landmarks divided the sample into 13 clusters and was thus found to be inappropriate for landmark data from species extremely resembling each other in morphology. Due to inconsistent results between the applied methods we refer to the traditional identifications and distinguish five rasborine species for the Mesangat wetlands.

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

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.