Bi-Atrial Statistical Shape Model and 100 Volumetric Anatomical Models

A bi-atrial statistical shape model, built from 47 MR and CT images, captures anatomical variation for in silico cardiac studies. The dataset includes 100 random volumetric model instances, each augmented with wall thickness, fiber orientation, and material tags for electrophysiological simulation readiness. An additional 95 geometries provide uniformly distributed left atrial volumes for studying atrial enlargement.

Use Cases

• Simulating atrial electrophysiology based on volumetric geometries with fiber orientation and material tags.
• Analyzing anatomical variation in atrial shape using a statistical shape model built from 47 medical images.
• Studying left atrial enlargement using the 95 provided geometries with uniformly distributed volumes.
• Validating computational models of ECG signals derived from detailed bi-atrial anatomical instances.

Strengths

• Model is derived from 47 real MR and CT images sourced from established segmentation challenges.
• Includes 100 ready-to-use volumetric instances, each with post-processed anatomical and electrophysiological features.
• Provides an additional 95 specific geometries for studying left atrial volume distribution.

Limitations

• Exact dataset size (e.g., file size, vertex count) and last update date are not specified in any source.
• The primary data format (H5, VTK) suggests the dataset is specialized and may require specific software for use.
• Metadata regarding the specific patient demographics or imaging parameters for the source images is not provided.

Provenance

Source

Publication: 'A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations' by Claudia Nagel et al. (Karlsruhe Institute of Technology).

Collection Method

Statistical shape model generated using ScalismoLab from 47 MR and CT images from three public challenges: Left Atrium Segmentation (2015), Left Atrium Fibrosis and Scar Segmentation (2013), and Left Atrial Wall Thickness (2018).

Time Range

Source imaging data spans challenges from 2013 to 2018.