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9 results for “Statistical shape modeling”
Development and validation of statistical shape models of the primary functional bone segments of the foot.
<p>This dataset comprises manually segmented three-dimensional point clouds (.STL) of magnetic resonance images of the primary functional segments of the foot - first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus. These data were used to create statistical shape models of the foot bones, utilising the GIAS2 toolbox (https://pypi.org/project/gias2/).</p>
A Bi-atrial Statistical Shape Model and 100 Volumetric Anatomical Models of the Atria
<p>This dataset is part of the publication "A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations" by Nagel et al. (<a href="https://doi.org/10.1016/j.media.2021.102210">https://doi.org/10.1016/j.media.2021.102210</a>). It includes a bi-atrial statistical shape model built based on 47 MR and CT images (Left atrium segmentation challenge (Tobon-Gomez, 2015), Left atrium fibrosis and scar segmentation challenge (Karim, 2013), Left atrial wall thickness challenge (Karim, 2018)). ScalismoLab (https://scalismo.org) was used for parts of the model generation. Further Details are explained in the paper. The SSM is available as an h5 file including information about the mean shape's vertex locations and their triangulation as well as the eigenvectors and -values. </p> <p>100 random instances derived from the model are available. Each zip file contains the volumetric bi-atrial geometry as vtk file, which was augmented in a post-processing step with a homogeneous wall thickness, fiber orientation, intra-atrial bridges and material tags so that they are ready to use for electrophysiological simulations of atrial signals. Furthermore, the scalar field resulting from computing the gradient of the Laplace equation with the boundary conditions described by Piersanti et al. (Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations, Computer Methods in Applied Mechanics and Engineering, 2020, <a href="https://doi.org/10.1016/j.cma.2020.113468">https://doi.org/10.1016/j.cma.2020.113468</a>) are available on the left and the right atrial instances. </p> <p>Furthermore, 95 geometries with uniformly distributed left atrial volumes are available in LAE_geometries.zip. </p>
A statistical shape model of craniosynostosis patients and 100 model instances of each pathology
<p>This dataset is part of the publication "A statistical shape model for radiation-free assessment and classification of craniosynostosis" (M. Schaufelberger et al.). It includes several 3D head models constructed of surface scans of craniosynostosis patients: The full shape model, a texture model, and submodels of four classes: sagittal suture fusion (scaphocephaly), metopic suture fusion (trigonocephaly), coronal suture fusion (brachycephaly and anterior plagiocephaly), and a control model (normocephaly and positional plagiocephaly). Each of the models is available in an .h5 file. We also include 100 mesh instances as a .ply file in a zip file. The model's statistical information can be incorporated into the [Liverpool-York child head model (Dai et al. 2019)](https://doi.org/10.1007/s11263-019-01260-7) as it uses the same vertex order and IDs (starting from index 0). If you want to synthesize new models, take a look a the demo.py file. For information about the hierarchy in the h5-file, take a look at documentation.md.</p>
Data from: High quality statistical shape modelling of the human nasal cavity and applications
The human nose is a complex organ that shows large morphological variations and has many important functions. However, the relation between shape and function is not yet fully understood. In this work, we present a high quality statistical shape model of the human nose based on clinical CT data of 46 patients. A technique based on cylindrical parametrization was used to create a correspondence between the nasal shapes of the population. Applying principal component analysis on these corresponded nasal cavities resulted in an average nasal geometry and geometrical variations, known as principal components, present in the population with a high precision. The analysis led to 46 principal components, which account for 95 percent of the total geometrical variation captured. These variations are first discussed qualitatively, and the effect on the average nasal shape of the first five principal components is visualized. Hereafter, by using this statistical shape model, two application examples that lead to quantitative data are shown: nasal shape in function of age and gender, and a morphometric analysis of different anatomical regions. Shape models, as the one presented here, can help to get a better understanding of nasal shape and variation, and their relationship with demographic data.
GAN, PCA, and Statistical Shape Models for the Creation of Synthetic Craniosynostosis Distance Maps
<p>This dataset is part of the publication "Classification of Craniosynostosis Trained Only On Synthetic Data Using GANs, PCA, and Statistical Shape Models".</p> <p><strong>dataset28.zip</strong> includes 2D distance maps constructed of surface scans of craniosynostosis patients: sagittal suture fusion (scaphocephaly), metopic suture fusion (trigonocephaly), coronal suture fusion (brachycephaly and anterior plagiocephaly), and a control model (normocephaly and positional plagiocephaly).<br> <br> <strong>synthetic_1000.zip</strong> contains are random 1000 samples per class created from each individual synthetic data source (GAN, PCA, statistical shape model).<br> <br> This repository contains only the images. To synthesize your own data, please use the github repository.</p>
Data from: High quality statistical shape modelling of the human nasal cavity and applications
Open the record for dataset details and reuse information.
Population average skull model obtained by means of statistical shape modelling
<p>Sagittal Craniosynostosis (SC) is a congenital condition whereby the newborn skull develops abnormally due to premature ossification of the sagittal suture. Spring-assisted cranioplasty (SAC) is a minimally invasive surgical technique to treat SC where metallic distractors are used to reshape the newborn's head. Although safe and effective, SAC outcomes remain uncertain due to the limited understanding of skull-distractor interaction and limited information provided by the analysis of single surgical cases.</p> <p>Hereby, an SC population average skull model was created to simulate spring insertion by means of finite element analysis.</p>
Population average skull model obtained by means of statistical shape modelling
Open the record for dataset details and reuse information.
Virtual Anatomical Reconstruction of Mandibular Bone Defects Using a Statistical Shape Model
ClinicalTrials.gov study NCT04475237. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.