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  1. Science and Research | Medical Devices

MIDA: A Multimodal Imaging-Based Model of the Human Head and Neck

Catalog of Regulatory Science Tools to Help Assess New Medical Devices

Technical Description

The MIDA model is a multimodal imaging-based detailed anatomical computer model of the human head and neck. The model offers detailed representation of brain surfaces, meninges, cerebrospinal fluid distribution, eyes, ears, and several deep brain structures, as well as many distinct muscles, bones and skull layers, blood vessels, cranial nerves, dental structures, and glands. Organs and tissues of the MIDA model are represented by three-dimensional, highly detailed computer-aided design (CAD) objects in standardized CAD data format. The individual CAD objects allow meshing at arbitrary resolutions without loss of small features. The MIDA model can be used in all software capable of importing and manipulating CAD data for the computational modelling in the device safety testing and design optimization.

The spatial resolution of the MIDA model is 500 μm isotropic. To enhance the visibility of specific tissues, the raw images were acquired using different magnetic resonance imaging (MRI) techniques:

  • structural T1- and T2-weighted sequences
  • a specific T2-weighted sequence with high nerve contrast optimized to enhance the structures of the ear and eye
  • magnetic resonance angiography (MRA) to image the vasculature
  • diffusion tensor imaging (DTI) to obtain information on tissue anisotropy and fiber orientation.

This model version includes a total of 116 structures (i.e., the 115 structures listed in Table 1 of Iacono et al. (2015) and the background [1].

Intended Purpose

The MIDA model can be used for computational modeling studies involving anatomically correct models of the human anatomy, e.g., for electromagnetic simulations. Of particular interest are computational simulations to potentially investigate the safety and efficacy of medical devices in, on, or near the head. For example, the MIDA model was used in case-studies involving non-invasive neurostimulation, i.e., transcranial alternating current stimulation (tACS) [1], as well as invasive neurostimulation, i.e., deep brain stimulation (DBS) [2,3].

The intended user population includes trainees in universities, medical device developers, and regulatory scientists. The tool can facilitate in-silico safety testing and design optimization for medical devices.


The tool has been characterized through both automatic and knowledge-based segmentation as described in [1]. The former method helps reduce the segmentation time and improve the quality of the results in terms of consistency, objectivity and reproducibility, while the latter method minimizes the errors resulting from automatic classification by including expert knowledge about the anatomy. Furthermore, inter-operator variability was assessed and showed to be non-significant and the results of the segmentation were reviewed by an expert anatomist [1].


  • The determination of absolute anatomical accuracy and precision in the outlining of the anatomical structures has some limitations due to the lack of a segmentation ground truth [1].
  • Segmentation errors may be due to the i) inadequacy of the images for the visualization of specific details, e.g., the lack of a specific MRI sequence for the deep brain structures, compensated partly by the integration of the Morel atlas, or inadequate spatial resolution for retina/choroid/sclera and vasculature imaging, e.g. minimal vessels diameters; ii) the presence of artifacts in the original data; iii) inaccuracies in the registration process used to estimate the alignment between MRA, eye/ear slab, and the non-isotropic DTI images, and in the atlas-based segmentation of the thalamus; iv) discrepancies in the definitions of anatomical structures in the available literature, e.g., about brainstem division and the outlining of pons/cerebellum boundary; v) additional limitations include the use of a finite number of discrete tissues, while, in reality, tissues show continuous variation and inhomogeneity [1].
  • The tool is subject-specific and does not take into account inter-individual anatomical variability. Currently, only a single head model has been generated [1].
  • The tool only provides head and neck tissues, thus users require to combine this tool with other whole-body models for the application necessary to take into consideration of the body loading effect [1].
  • The tool contains complex anatomical details, a degree of tissue simplification may be required to convert mesh-type model (i.e., stl) into solid format (e.g., .iges) to be used in the finite-element-method based software.
  • The tool is an anatomical model only. Application-specific verification and validation may be needed to analyze the overall credibility of the model for specific uses (e.g., following the framework provided in the FDA recognized standard ASME V&V 40-2018 Assessing Credibility of Computational Modeling Through Verification and Validation: Application to Medical Devices)

Supporting Documentation

Tool websites:

MIDA Model (itis.swiss)

DOI: 10.13099/ViP-MIDA-V1.0

1.  MIDA: A Multimodal Imaging-Based Detailed Anatomical Model of the Human Head and Neck Maria Ida Iacono, Esra Neufeld, Esther Akinnagbe, Kelsey Bower, Johanna Wolf, Ioannis Oikonomidis, Deepika Sharma, Bryn Lloyd, Bertram Wilm, Michael Wyss, Klaas Pruessmann, Andras Jakab Nikos Makris, Ethan Cohen, Niels Kuster, Wolfgang Kainz, and Leonardo M. Angelone, in PLoS ONE 10(4): e0124126. doi:10.1371/journal.pone.0124126, April 2015

2.  A computational model for bipolar deep brain stimulation of the subthalamic nucleus Maria Ida Iacono, Esra Neufeld, Bonmassar Giorgio, Esther Akinnagbe, Andras Jakab, Ethan Cohen, Niels Kuster, Wolfgang Kainz and Leonardo Angelone, in IEEE Engineering in Medicine and Biology Society, pp. 6258-61, 2014

3.  Simulation platform for coupled modeling of EM-induced neuronal dynamics and functionalized anatomical models Neufeld Esra, Ioannis Oikonomidis, Maria Ida Iacono, Esther Akinnagbe, Leonardo Angelone, Wolfgang Kainz and Niels Kuster, in 7th International IEEE EMBS Neural Engineering Conference, 2015


Tool Reference

In addition to citing relevant publications please reference the use of this tool using DOI: 10.5281/zenodo.8228899.

For more information:

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