Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multiple independent scans. To accurately segment the hippocampal morphology from longitudinal 3T T1-weighted MR images, we propose a diffeomorphic geodesic guided deep learning method called the GeoLongSeg to mitigate the longitudinal variabilities that unrelated to diseases by enhancing intra-individual morphological consistency. Specifically, we integrate geodesic shape regression, an evolutional model that estimates smooth deformation process of anatomical shapes, into a two-stage segmentation network. We adopt a 3D U-Net in the first-stage network with an enhanced attention mechanism for independent segmentation. Then, a hippocampal shape evolutional trajectory is estimated by geodesic shape regression and fed into the second network to refine the independent segmentation. We verify that GeoLongSeg outperforms other four state-of-the-art segmentation pipelines in longitudinal morphological consistency evaluated by test-retest reliability, variance ratio and atrophy trajectories. When assessing hippocampal atrophy in longitudinal data from the Alzheimer\'s Disease Neuroimaging Initiative (ADNI), results based on GeoLongSeg exhibit spatial and temporal local atrophy in bilateral hippocampi of dementia patients. These features derived from GeoLongSeg segmentation exhibit the greatest discriminatory capability compared to the outcomes of other methods in distinguishing between patients and normal controls. Overall, GeoLongSeg provides an accurate and efficient segmentation network for extracting hippocampal morphology from longitudinal MR images, which assist precise atrophy measurement of the hippocampus in early stage of dementia.

A geometric morphometric analysis was performed on the right wing of adult Calliphora vicina (Robineau-Desvoidy) collected across 4 altitudinal levels in Sicily. The objective of this study was to assess differences in shape and centroid size (CS) between females and males and across elevations. The wings analyzed in this study were removed from adults of C. vicina collected with baited traps at 20, 700, 1,153, and 1,552; for this study, 19 landmarks were identified in each wing. The coordinates of the landmarks were aligned and superimposed to prevent variations due to position, orientation, and scale; they were then scaled to the same CS and recentered. CS and Procrustes differences were, respectively, used to assess variations in size and shape. Significant differences were observed in wing shape between males and females but not between all altitudinal levels. Female wings were found to be significantly larger than males (P < 0.01). Wings of flies collected at the highest altitudinal level resulted in significantly larger wings than those collected at lower altitudes (P < 0.001), with CS values ranging from 12.1 to 14.1. Variation in wing shape can impact thermal regulation, and therefore, oxygen content, temperature, atmospheric pressure, and solar radiation can have an effect on an insect\'s body and activity levels. At high elevations and lower temperatures, larger wings could mean less energy expenditure when flying to increase body temperature.

Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee, a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they\'re also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.

The morphology and function of the cerebellum are associated with various developmental disorders and healthy aging. Changes in cerebellar morphology during the aging process have been extensively investigated, with most studies focusing on changes in cerebellar regional volume. The volumetric method has been used to quantitatively demonstrate the decrease in the cerebellar volume with age, but it has certain limitations in visually presenting the morphological changes of cerebellar atrophy from a three-dimensional perspective. Thus, we comprehensively described cerebellar morphological changes during aging through volume measurements of subregions and shape analysis. This study included 553 healthy participants aged 20-80 years. A novel cerebellar localized segmentation algorithm based on convolutional neural networks was utilized to analyze the volume of subregions, followed by shape analysis for localized atrophy assessment based on the cerebellar thickness. The results indicated that out of the 28 subregions in the absolute volume of the cerebellum, 15 exhibited significant aging trends, and 16 exhibited significant sex differences. Regarding the analysis of relative volume, only 11 out of the 28 subregions of the cerebellum exhibited significant aging trends, and 4 exhibited significant sex differences. The results of the shape analysis revealed region-specific atrophy of the cerebellum with increasing age. Regions displaying more significant atrophy were predominantly located in the vermis, the lateral portions of bilateral cerebellar hemispheres, lobules I-III, and the medial portions of the posterior lobe. This atrophy differed between sexes. Men exhibited slightly more severe atrophy than women in most of the cerebellar regions. Our study provides a comprehensive perspective for observing cerebellar atrophy during the aging process.

Morphological adaptation is the change in the form of an organism that benefits the individual in its current habitat. Mole-rats (family Bathyergidae), despite being subterranean, are impacted by both local and broad-scale environmental conditions that occur above ground. Common mole-rats (Cryptomys hottentotus hottentotus) present an ideal mammalian model system for the study of morphological variation in response to ecology, as this species is found along an aridity gradient and thus can be sampled from geographically non-overlapping populations of the same species along an environmental longitudinal cline. Using the mass of five internal organs, ten skeletal measurements and 3D morphometric analyses of skulls, we assessed the morphology of wild non-breeding individuals from five common mole-rat populations in South Africa. We found that the body mass and mean relative mass of the spleen and kidneys in arid populations was larger, and individuals from arid regions possessed shorter legs and larger inter-shoulder widths compared to individuals from mesic regions. Additionally, arid populations demonstrated greater skull depth, and shape change of features such as angular processes of the lower jaw than mesic individuals, indicating that these distinct geographic populations show differences corresponding to the aridity gradient, potentially in response to environmental factors such as the variation in food sources found between different habitats, in addition to different soil compositions found in the different regions. Arid populations potentially require a stronger jaw and neck musculature associated with mastication to chew xeric-adapted plants and to dig through hard soil types, whereas mesic populations excavate through soft, looser soil and may make use of their front limbs to aid the movement of soils when digging. Aridity influences the morphology of this species and could indicate the impact of environmental changes on speciation and mammalian skull morphology.

We describe coordinate systems adapted for the space between two surfaces, such as those delineating the highly folded cortex in mammalian brains. These systems are estimated in order to satisfy geometric priors, including streamline normality or equivolumetric conditions on layers. We give a precise mathematical formulation of these problems, and present numerical simulations based on diffeomorphic registration methods, comparing them with recent approaches. Our method involves•Diffeomorphic registration of inner and outer folded folded surfaces.•Followed by equivolumetric reparametrization of layers to yield coordinate system.

This paper examines a longitudinal shape evolution model in which a three-dimensional volume progresses through a family of elastic equilibria in response to the time-derivative of an internal force, or yank, with an additional regularization to ensure diffeomorphic transformations. We consider two different models of yank and address the long time existence and uniqueness of solutions for the equations of motion in both models. In addition, we derive sufficient conditions for the existence of an optimal yank that best describes the change from an observed initial volume to an observed volume at a later time. The main motivation for this work is the understanding of processes such as growth and atrophy in anatomical structures, where the yank could be roughly interpreted as a metabolic event triggering morphological changes. We provide preliminary results on simple examples to illustrate, under this model, the retrievability of some attributes of such events.

Cortical thickness is an important biomarker associated with gray matter atrophy in neurodegenerative diseases. In order to conduct meaningful comparisons of cortical thickness between different subjects, it is imperative to establish correspondence among surface meshes. Conventional methods achieve this by projecting surface onto canonical domains such as the unit sphere or averaging feature values in anatomical regions of interest (ROIs). However, due to the natural variability in cortical topography, perfect anatomically meaningful one-to-one mapping can be hardly achieved and the practice of averaging leads to the loss of detailed information. For example, two subjects may have different number of gyral structures in the same region, and thus mapping can result in gyral/sulcal mismatch which introduces noise and averaging in detailed local information loss. Therefore, it is necessary to develop new method that can overcome these intrinsic problems to construct more meaningful comparison for atrophy detection. To address these limitations, we propose a novel personalized patch-based method to improve cortical thickness comparison across subjects. Our model segments the brain surface into patches based on gyral and sulcal structures to reduce mismatches in mapping method while still preserving detailed topological information which is potentially discarded in averaging. Moreover,the personalized templates for each patch account for the variability of folding patterns, as not all subjects are comparable. Finally, through normality assessment experiments, we demonstrate that our model performs better than standard spherical registration in detecting atrophy in patients with mild cognitive impairment (MCI) and Alzheimer\'s disease (AD).

The garfish Belone belone represents the only valid endemic Belone species for the Mediterranean Sea and the eastern Atlantic Ocean. It shows a wide global distribution range, with a high commercial value and ecological relevance in the pelagic domain. Despite this, there needs to be more knowledge regarding the otoliths of this species, with the total absence of descriptions regarding asterisci and lapilli from Mediterranean populations and a lack of studies on the reliability of shape analysis on its sagittae. The present paper aims to provide the first main contours description of the three otoliths pairs from a Mediterranean population, providing an accurate investigation of morphology, morphometry, and intra-specific variability of sagittae, lapilli, and asterisci. Results showed (i) the absence of directional bilateral asymmetry and sexual asymmetry for the three otoliths pairs, (ii) a different morphology and morphometry of sagittae, lapilli and asterisci than those described in the literature, and (iii) an enhanced variability between sagittae morphometry and shape between the three investigated size classes. All these data confirmed the reliability of the studied species of shape analysis, showing a geographical and size-related variability of otoliths features probably related to genetics, environmental conditions, and life habits variations.

In this study, we introduce a novel approach for the analysis and interpretation of 3D shapes, particularly applied in the context of neuroscientific research. Our method captures 2D perspectives from various vantage points of a 3D object. These perspectives are subsequently analyzed using 2D Convolutional Neural Networks (CNNs), uniquely modified with custom pooling mechanisms. We sought to assess the efficacy of our approach through a binary classification task involving subjects at high risk for Autism Spectrum Disorder (ASD). The task entailed differentiating between high-risk positive and high-risk negative ASD cases. To do this, we employed brain attributes like cortical thickness, surface area, and extra-axial cerebral spinal measurements. We then mapped these measurements onto the surface of a sphere and subsequently analyzed them via our bespoke method. One distinguishing feature of our method is the pooling of data from diverse views using our icosahedron convolution operator. This operator facilitates the efficient sharing of information between neighboring views. A significant contribution of our method is the generation of gradient-based explainability maps, which can be visualized on the brain surface. The insights derived from these explainability images align with prior research findings, particularly those detailing the brain regions typically impacted by ASD. Our innovative approach thereby substantiates the known understanding of this disorder while potentially unveiling novel areas of study.