SHARM: Segmented Head Anatomical Reference Models

TL;DR

This paper introduces SHARM, an open-access set of 196 segmented head models covering 15 tissues, created using CNNs, to support diverse clinical and research applications involving head anatomy.

Contribution

It provides the first large, publicly available, multi-tissue head segmentation dataset using CNNs, filling a gap in existing resources for head tissue modeling.

Findings

High tissue segmentation consistency across age groups

SHARM enables improved electromagnetic and clinical head studies

Open access facilitates broader research and validation

Abstract

Reliable segmentation of anatomical tissues of human head is a major step in several clinical applications such as brain mapping, surgery planning and associated computational simulation studies. Segmentation is based on identifying different anatomical structures through labeling different tissues through medical imaging modalities. The segmentation of brain structures is commonly feasible with several remarkable contributions mainly for medical perspective; however, non-brain tissues are of less interest due to anatomical complexity and difficulties to be observed using standard medical imaging protocols. The lack of whole head segmentation methods and unavailability of large human head segmented datasets limiting the variability studies, especially in the computational evaluation of electrical brain stimulation (neuromodulation), human protection from electromagnetic field, and electroencephalography where non-brain tissues are of great importance. To fill this gap, this study provides an open-access Segmented Head Anatomical Reference Models (SHARM) that consists of 196 subjects. These models are segmented into 15 different tissues; skin, fat, muscle, skull cancellous bone, skull cortical bone, brain white matter, brain gray matter, cerebellum white matter, cerebellum gray matter, cerebrospinal fluid, dura, vitreous humor, lens, mucous tissue and blood vessels. The segmented head models are generated using open-access IXI MRI dataset through convolutional neural network structure named ForkNet+. Results indicate a high consistency in statistical characteristics of different tissue distribution in age scale with real measurements. SHARM is expected to be a useful benchmark not only for electromagnetic dosimetry studies but also for different human head segmentation applications.