Red blood cell phenotyping from 3D confocal images using artificial neural networks

The investigation of cell shapes mostly relies on the manual classification of 2D images, causing a subjective and time consuming evaluation based on a portion of the cell surface. We present a dual-stage neural network architecture for analyzing fine shape details from confocal microscopy recordings in 3D. The system, tested on red blood cells, uses training data from both healthy donors and patients with a congenital blood disease, namely hereditary spherocytosis. Characteristic shape features are revealed from the spherical harmonics spectrum of each cell and are automatically processed to create a reproducible and unbiased shape recognition and classification. The results show the relation between the particular genetic mutation causing the disease and the shape profile. With the obtained 3D phenotypes, we suggest our method for diagnostics and theragnostics of blood diseases. Besides the application employed in this study, our algorithms can be easily adapted for the 3D shape phenotyping of other cell types and extend their use to other applications, such as industrial automated 3D quality control.     

Perception of shape-from-motion in macaque monkeys and humans

Motion is one of the most efficient cues for shape perception. We conducted behavioral experiments to examine how monkeys perceive shapes defined by motion cues and whether they perceive them as humans do. We trained monkeys to perform a shape discrimination task in which shapes were defined by the motion of random dots. Effects of dot density and dot speed on the shape perception of monkeys were examined. Human subjects were also tested using the same paradigm and the test results were compared with those of monkeys. In both monkeys and humans, correct performance rates declined when density or speed of random dots was reduced. Both of them tended to confuse the same combinations of shapes frequently. These results suggest that monkeys and humans perceive shapes defined by motion cues in a similar manner and probably have common neural mechanisms to perceive them. Electronic Publication     

Multivariate patterns in object-selective cortex dissociate perceptual and physical shape similarity

Prior research has identified the lateral occipital complex (LOC) as a critical cortical region for the representation of object shape in humans. However, little is known about the nature of the representations contained in the LOC and their relationship to the perceptual experience of shape. We used human functional MRI to measure the physical, behavioral, and neural similarity between pairs of novel shapes to ask whether the representations of shape contained in subregions of the LOC more closely reflect the physical stimuli themselves, or the perceptual experience of those stimuli. Perceptual similarity measures for each pair of shapes were obtained from a psychophysical same-different task; physical similarity measures were based on stimulus parameters; and neural similarity measures were obtained from multivoxel pattern analysis methods applied to anterior LOC (pFs) and posterior LOC (LO). We found that the pattern of pairwise shape similarities in LO most closely matched physical shape similarities, whereas shape similarities in pFs most closely matched perceptual shape similarities. Further, shape representations were similar across participants in LO but highly variable across participants in pFs. Together, these findings indicate that activation patterns in subregions of object-selective cortex encode objects according to a hierarchy, with stimulus-based representations in posterior regions and subjective and observer-specific representations in anterior regions.     

Distributed neural plasticity for shape learning in the human visual cortex

Expertise in recognizing objects in cluttered scenes is a critical skill for our interactions in complex environments and is thought to develop with learning. However, the neural implementation of object learning across stages of visual analysis in the human brain remains largely unknown. Using combined psychophysics and functional magnetic resonance imaging (fMRI), we show a link between shape-specific learning in cluttered scenes and distributed neuronal plasticity in the human visual cortex. We report stronger fMRI responses for trained than untrained shapes across early and higher visual areas when observers learned to detect low-salience shapes in noisy backgrounds. However, training with high-salience pop-out targets resulted in lower fMRI responses for trained than untrained shapes in higher occipitotemporal areas. These findings suggest that learning of camouflaged shapes is mediated by increasing neural sensitivity across visual areas to bolster target segmentation and feature integration. In contrast, learning of prominent pop-out shapes is mediated by associations at higher occipitotemporal areas that support sparser coding of the critical features for target recognition. We propose that the human brain learns novel objects in complex scenes by reorganizing shape processing across visual areas, while taking advantage of natural image correlations that determine the distinctiveness of target shapes.     

3D genital shape complexity in female marine mammals

Comparisons of 3D shapes have recently been applied to diverse anatomical structures using landmarking techniques. However, discerning evolutionary patterns can be challenging for structures lacking homologous landmarks. We used alpha shape analyses to quantify vaginal shape complexity in 40 marine mammal specimens including cetaceans, pinnipeds, and sirenians. We explored phylogenetic signal and the potential roles of natural and sexual selection on vaginal shape evolution. Complexity scores were consistent with qualitative observations. Cetaceans had a broad range of alpha complexities, while pinnipeds were comparatively simple and sirenians were complex. Intraspecific variation was found. Three‐dimensional surface heat maps revealed that shape complexity was driven by invaginations and protrusions of the vaginal wall. Phylogenetic signal was weak and metrics of natural selection (relative neonate size) and sexual selection (relative testes size, sexual size dimorphism, and penis morphology) did not explain vaginal complexity patterns. Additional metrics, such as penile shape complexity, may yield interesting insights into marine mammal genital coevolution. We advocate for the use of alpha shapes to discern patterns of evolution that would otherwise not be possible in 3D anatomical structures lacking homologous landmarks.     

Visual shape representation with geometrically characterized contour partitions

This paper proposes a biologically plausible matching method to recognize general shapes based on contour curvature information. The human visual system recognizes general shapes flexibly in real-world scenes through the ventral pathway. The pathway is typically modeled using artificial neural networks. These network models, however, do not construct a shape representation that satisfies the following required constraints: (1) The original shape should be represented by a group of partitioned contours in order to retrieve the whole shape (global information) from the partial contours (local information). (2) Coarse and fine structures of the original shapes should be individually represented in order for the visual system to respond to shapes as quickly as possible based on the least number of their features, and to discriminate between shapes based on detailed information. (3) The shape recognition realized with an artificial visual system should be invariant to geometric transformation such as expansion, rotation, or shear. In this paper, we propose a visual shape representation with geometrically characterized contour partitions described on multiple spatial scales.     

3D surface analysis and classification in neuroimaging segmentation

This work emphasizes new algorithms for 3D edge and corner detection used in surface extraction and new concept of image segmentation in neuroimaging based on multidimensional shape analysis and classification. We propose using of NifTI standard for describing input data which enables interoperability and enhancement of existing computing tools used widely in neuroimaging research. In methods section we present our newly developed algorithm for 3D edge and corner detection, together with the algorithm for estimating local 3D shape. Surface of estimated shape is analyzed and segmented according to kernel shapes.     

Human L3L4 intervertebral disc mean 3D shape,modes of variation,and their relationship to degeneration

Intervertebral disc mechanics are affected by both disc shape and disc degeneration, which in turn each affect the other; disc mechanics additionally have a role in the etiology of disc degeneration. Finite element analysis (FEA) is a favored tool to investigate these relationships, but limited data for intervertebral disc 3D shape has forced the use of simplified or single-subject geometries, with the effect of inter-individual shape variation investigated only in specialized studies. Similarly, most data on disc shape variation with degeneration is based on 2D mid-sagittal images, which incompletely define 3D shape changes. Therefore, the objective of this study was to quantify inter-individual disc shape variation in 3D, classify this variation into independently-occurring modes using a statistical shape model, and identify correlations between disc shape and degeneration. Three-dimensional disc shapes were obtained from MRI of 13 human male cadaver L3L4 discs. An average disc shape and four major modes of shape variation (representing 90% of the variance) were identified. The first mode represented disc axial area and was significantly correlated to degeneration (R²=0.44), indicating larger axial area in degenerate discs. Disc height variation occurred in three distinct modes, each also involving non-height variation. The statistical shape model provides an average L3L4 disc shape for FEA that is fully defined in 3D, and makes it convenient to generate a set of shapes with which to represent aggregate inter-individual variation. Degeneration grade-specific shapes can also be generated. To facilitate application, the model is included in this paper?s supplemental content.     

Exploring the functional meaning of head shape disparity in aquatic snakes

Phenotypic diversity, or disparity, can be explained by simple genetic drift or, if functional constraints are strong, by selection for ecologically relevant phenotypes. We here studied phenotypic disparity in head shape in aquatic snakes. We investigated whether conflicting selective pressures related to different functions have driven shape diversity and explore whether similar phenotypes may give rise to the same functional output (i.e., many‐to‐one mapping of form to function). We focused on the head shape of aquatically foraging snakes as they fulfill several fitness‐relevant functions and show a large amount of morphological variability. We used 3D surface scanning and 3D geometric morphometrics to compare the head shape of 62 species in a phylogenetic context. We first tested whether diet specialization and size are drivers of head shape diversification. Next, we tested for many‐to‐one mapping by comparing the hydrodynamic efficiency of head shape characteristic of the main axes of variation in the dataset. We 3D printed these shapes and measured the forces at play during a frontal strike. Our results show that diet and size explain only a small amount of shape variation. Shapes did not fully functionally converge as more specialized aquatic species evolved a more efficient head shape than others. The shape disparity observed could thus reflect a process of niche specialization.     

A Shared Pattern of Postnatal Endocranial Development in Extant Hominoids

By comparing species-specific developmental patterns, we can approach the question of how development shapes adult morphology and contributes to the evolution of novel forms. Studies of evolutionary changes to brain development in primates can provide important clues about the emergence of human cognition, but are hindered by the lack of preserved neural tissue in the fossil record. As a proxy, we study the shape of endocasts, virtual imprints of the endocranial cavity, using 3D geometric morphometrics. We have previously demonstrated that the pattern of endocranial shape development is shared by modern humans, chimpanzees and Neanderthals after the first year of life until adulthood. However, whether this represents a common hominoid mode of development is unknown. Here, we present the first characterization and comparison of ontogenetic endocranial shape changes in a cross-sectional sample of modern humans, chimpanzees, gorillas, orangutans and gibbons. Using developmental simulations, we demonstrate that from late infancy to adulthood ontogenetic trajectories are similar among all hominoid species, but differ in the amount of shape change. Furthermore, we show that during early ontogeny gorillas undergo more pronounced shape changes along this shared trajectory than do chimpanzees, indicative of a dissociation of size and shape change. As shape differences between species are apparent in even our youngest samples, our results indicate that the ontogenetic trajectories of extant hominoids diverged at an earlier stage of ontogeny but subsequently converge following the eruption of the deciduous dentition.     

Neural response to perception of volume in the lateral occipital complex

Projection of a 3D scene onto the 2D retina necessarily entails a loss of information, yet perceivers experience a world populated with volumetric objects. Using simultaneous behavioral and neural (fMRI) measures, we identify neural bases of volume perception. Neural activity in the lateral occipital cortex increased with presentation of 3D volumes relative to presentation of 2D shapes. Neural activity also modulated with perceived volume, independent of image information. When behavioral responses indicated that observers saw ambiguous images as 3D volumes, neural response increased; when behavioral data revealed a 2D interpretation, neural response waned. Crucially, the physical stimulus was identical under both interpretations; only the percept of volume can account for the increased neural activity.     

Joint modeling of cell and nuclear shape variation

Modeling cell shape variation is critical to our understanding of cell biology. Previous work has demonstrated the utility of nonrigid image registration methods for the construction of nonparametric nuclear shape models in which pairwise deformation distances are measured between all shapes and are embedded into a low-dimensional shape space. Using these methods, we explore the relationship between cell shape and nuclear shape. We find that these are frequently dependent on each other and use this as the motivation for the development of combined cell and nuclear shape space models, extending nonparametric cell representations to multiple-component three-dimensional cellular shapes and identifying modes of joint shape variation. We learn a first-order dynamics model to predict cell and nuclear shapes, given shapes at a previous time point. We use this to determine the effects of endogenous protein tags or drugs on the shape dynamics of cell lines and show that tagged C1QBP reduces the correlation between cell and nuclear shape. To reduce the computational cost of learning these models, we demonstrate the ability to reconstruct shape spaces using a fraction of computed pairwise distances. The open-source tools provide a powerful basis for future studies of the molecular basis of cell organization.     

Structural analysis of interphase X-chromatin based on statistical shape theory

The 3D folding structure formed by different genomic regions of a chromosome is still poorly understood. So far, only relatively simple geometric features, like distances and angles between different genomic regions, have been evaluated. This work is concerned with more complex geometric properties, i.e., the complete shape formed by genomic regions. Our work is based on statistical shape theory and we use different approaches to analyze the considered structures, e.g., shape uniformity test, 3D point-based registration, Fisher distribution, and 3D non-rigid image registration for shape normalization. We have applied these approaches to analyze 3D microscopy images of the X-chromosome where four consecutive genomic regions (BACs) have been simultaneously labeled by multicolor FISH. We have acquired two sets of four consecutive genomic regions with an overlap of three regions. From the experimental results, it turned out that for all data sets the complete structure is non-random. In addition, we found that the shapes of active and inactive X-chromosomal genomic regions are statistically independent. Moreover, we reconstructed the average 3D structure of chromatin in a small genomic region (below 4 Mb) based on five BACs resulting from two overlapping four BAC regions. We found that geometric normalization with respect to the nucleus shape based on non-rigid image registration has a significant influence on the location of the genomic regions.     

Signals in Stochastically Generated Neurons

To incorporate variation of neuron shape in neural models, we developed a method of generating a population of realistically shaped neurons. Parameters that characterize a neuron include soma diameters, distances to branch points, fiber diameters, and overall dendritic tree shape and size. Experimentally measured distributions provide a means of treating these morphological parameters as stochastic variables in an algorithm for production of neurons. Stochastically generated neurons shapes were used in a model of hippocampal dentate gyrus granule cells. A large part of the variation of whole neuron input resistance RN is due to variation in shape. Membrane resistivity Rm computed from RN varies accordingly. Statistics of responses to synaptic activation were computed for different dendritic shapes. Magnitude of response variation depended on synapse location, measurement site, and attribute of response.     

Medial axis shape coding in macaque inferotemporal cortex

The basic, still unanswered question about visual object representation is this: what specific information is encoded by neural signals? Theorists have long predicted that neurons would encode medial axis or skeletal object shape, yet recent studies reveal instead neural coding of boundary or surface shape. Here, we addressed this theoretical/experimental disconnect, using adaptive shape sampling to demonstrate explicit coding of medial axis shape in high-level object cortex (macaque monkey inferotemporal cortex or IT). Our metric shape analyses revealed a coding continuum, along which most neurons represent a configuration of both medial axis and surface components. Thus, IT response functions embody a rich basis set for simultaneously representing skeletal and external shape of complex objects. This would be especially useful for representing biological shapes, which are often characterized by both complex, articulated skeletal structure and specific surface features.     

A semi-localized elastic net for surface reconstruction of objects from multislice images

The traveling salesman problem (TSP) is a prototypical problem of combinatorial optimization and, as such, it has received considerable attention from neural-network researchers seeking quick, heuristic solutions. An early stage in many computer vision tasks is the extraction of object shape from an image consisting of noisy candidate edge points. Since the desired shape will often be a closed contour, this problem can be viewed as a version of the TSP in which we wish to link only a subset of the points/cities (i.e. the "noisefree" ones). None of the extant neural techniques for solving the TSP can deal directly with this case. In this paper, we present a simple but effective modification to the (analog) elastic net of Durbin and Willshaw which shifts emphasis from global to local behavior during convergence, so allowing the net to ignore some image points. Unlike the original elastic net, this semi-localized version is shown to tolerate considerable amounts of noise. As an example practical application, we describe the extraction of "pseudo-3D" human lung outlines from multiple preprocessed magnetic resonance images of the torso. An effectiveness measure (ideally zero) quantifies the difference between the extracted shape and some idealized shape exemplar. Our method produces average effectiveness scores of 0.06 for lung shapes extracted from initial semi-automatic segmentations which define the noisefree case. This deteriorates to 0.1 when extraction is from a noisy edge-point image obtained fully-automatically using a feedforward neural network.     

Biomechanical study on the edge shapes for penetrating keratoplasty

A parametric study to investigate the compressive and the shear stress distributions for various edge shapes created during penetrating keratoplasty (PK) using femtosecond laser is reported. The finite element analysis has been implemented using ABAQUS to study the cornea with various edge shapes, namely the standard edge shape, the zigzag edge shape, the top hat edge shape and the mushroom edge shape for PK. The ratio of maximum compressive stress to maximum shear stress is used as the main factor to assess the relative merits of wound healing rate for different edge shapes. For the typical values of tissue mechanical properties, the zigzag edge shape has the highest ratio of maximum compressive stress to maximum shear stress (11.1 in the xy-direction and 3.7 in the yz-direction), followed by the mushroom edge shape (7.7 in the xy-direction and 3.2 in the yz-direction). The ratios for the top hat and the standard edge shapes are even lower in both directions. A sensitivity analysis of the model has been done to demonstrate that the zigzag edge shape always results in the highest ratios of stresses regardless of the difference in the tissue mechanical properties. The zigzag edge shape also gives the lowest dioptric power D?=?45.4. The present results imply that the zigzag edge shape provides the best wound healing rate and optical outcome among the four edge shapes models for PK.     

Spatial modulation of primate inferotemporal responses by eye position

Background

A key aspect of representations for object recognition and scene analysis in the ventral visual stream is the spatial frame of reference, be it a viewer-centered, object-centered, or scene-based coordinate system. Coordinate transforms from retinocentric space to other reference frames involve combining neural visual responses with extraretinal postural information.

Methodology/Principal Findings

We examined whether such spatial information is available to anterior inferotemporal (AIT) neurons in the macaque monkey by measuring the effect of eye position on responses to a set of simple 2D shapes. We report, for the first time, a significant eye position effect in over 40% of recorded neurons with small gaze angle shifts from central fixation. Although eye position modulates responses, it does not change shape selectivity.

Conclusions/Significance

These data demonstrate that spatial information is available in AIT for the representation of objects and scenes within a non-retinocentric frame of reference. More generally, the availability of spatial information in AIT calls into questions the classic dichotomy in visual processing that associates object shape processing with ventral structures such as AIT but places spatial processing in a separate anatomical stream projecting to dorsal structures.     

The morphogenesis of liposomes viewed from the aspect of bending energy

It is known that liposomes transform their shapes sequentially through one of several transformation pathways. Using the mechanical principle of the least bending energy of membranes, we investigate the stability and shape transformation of liposomes with geometrical symmetry. We have done this by computer simulations and theoretical analyses, in which three-dimensional liposome shapes have been generated by the modified Cassini equation. We show first that there are energetically stable liposome shapes having intrinsic geometrical symmetry. We find that by reducing the volume, the stable shape can change from a circular biconcave shape as in red blood cells, to elliptical, triangular, square, and other polygonal shapes. It is also found that the preceding two results hold true irrespective of the overall surface area of liposome.