Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach

Abstract

Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource.

Advancing Thalamic Nuclei Segmentation: The Impact of Compressed Sensing and FastSurfer on MRI Processing

“…Hence, the present study fills this gap showing that good image quality can be achieved with CS accelerations also in structures as relatively small as thalamic nuclei, whose borders can be particularly elusive to 3T structural MRI contrasts and parcellation techniques (Magnotta et al, 2000;Iglesias et al, 2018;Najdenovska et al, 2019;Su et al, 2019;Rushmore et al, 2022).…”

“…Magnetic resonance imaging (MRI)-based structural and volumetric characterization of thalamic nuclei in humans has become increasingly important for both basic research and clinical purposes (Lozano, 2000;Iglesias et al, 2018;Keun et al, 2021). Nevertheless, in vivo mapping of thalamic nuclei can present technical challenges, since thalamic nuclear boundaries are notoriously difficult to visualize, for instance, even in standard T1-weighted (T1w) MRI (Magnotta et al, 2000;Iglesias et al, 2018;Najdenovska et al, 2019;Su et al, 2019;Rushmore et al, 2022). Another challenge can result from structural and volumetric biases induced by high levels of head motion during MRI (Reuter et al, 2015;Baum et al, 2018;Zacà et al, 2018), such as those occurring in patients with tremor, Alzheimer's diseases, and also healthy elderly adults (Van Dijk et al, 2012;Iglesias et al, 2017).…”

A Proposed Human Structural Brain Connectivity Matrix in the Center for Morphometric Analysis Harvard-Oxford Atlas Framework: A Historical Perspective and Future Direction for Enhancing the Precision of Human Structural Connectivity with a Novel Neuroanatomical Typology

“…Furthermore, it has been implemented in functional MRI (e.g., [78,79]), lesion analysis (e.g., [80][81][82]), and diffusion imaging (e.g., [4,68,71,76,83,84]). Its implementations are both user-guided (e.g., [4,6,63,85,86]) and automated (e.g., [87,88]). Moreover, this system has been used as the basis for a number of subsequent neuroanatomical variations (e.g., [86,[89][90][91][92]; see [92] for review).…”

“…Its implementations are both user-guided (e.g., [4,6,63,85,86]) and automated (e.g., [87,88]). Moreover, this system has been used as the basis for a number of subsequent neuroanatomical variations (e.g., [86,[89][90][91][92]; see [92] for review). Inferences from the monkey literature can be made relative to putative homologies between monkey and human cortical and subcortical areas (e.g., [65,93,94]).…”