Imaging Atlas Of Human Anatomy
Chloe Sarnoff
e-Anatomy is a high-quality anatomy and imaging content atlas. It is the most complete reference of human anatomy available on the Web, iPad, iPhone and Android devices. Explore detailed anatomical views and multiple modalities (over 8,900 anatomic structures and more than 870,000 translated medical labels) with images in CT, MRI, radiographs, anatomical diagrams and nuclear images. Available in 12 languages.
IMAIOS is a company which aims to assist and train human and animal practitioners. Serving healthcare professionals through interactive anatomy atlases, medical imaging, collaborative database of clinical cases, online courses...
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Imaging is ever more integral to anatomy education and throughout modern medicine. Building on the success of previous editions, this fully revised sixth edition provides a superb foundation for understanding applied human anatomy, offering a complete view of the structures and relationships within the whole body, using the very latest imaging techniques.
This superb package is ideally suited to the needs of medical students, as well as radiologists, radiographers and surgeons in training. It will also prove invaluable to the range of other students and professionals who require a clear, accurate, view of anatomy in current practice.
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The hypothalamus is an intricate neuroanatomical structure that occupies 0.3% of the adult human brain volume1. Despite its comparatively small size it plays a crucial role in the homeostasis of neuroendocrine, behavioral, and autonomic processes essential to life2. Through a vast array of neural connections, it receives input from both spinal cord and brainstem nuclei as well as corticolimbic structures. This information is integrated within dedicated hypothalamic nuclei, altered through secondary and tertiary hypothalamic relay stations and then conveyed to autonomic and limbic control centers where the appropriate physiological response is orchestrated across organ systems3. Apart from axonal projections, the hypothalamus further shapes the physiological response via engagement of the pituitary gland and release of hormones into the bloodstream4.
Owing to its vital role, the hypothalamus is of great clinical and scientific interest. It has been implicated in a wide range of neurological, endocrinological and psychiatric diseases, is a common drug target, and finds surgical exposure in neurooncology and functional neurosurgery5. To understand the causal underpinnings of hypothalamic dysfunction, an extensive body of research is dedicated towards establishing the structural and functional relationships within the hypothalamus proper as well as the local and global structural connectivity wherein its nuclei are embedded6,7,8. In the field of neuroimaging, this effort is primarily directed towards volumetric and topological analyses in healthy and diseased states9,10,11. Conversely, probing of the hypothalamus by means of electrical stimulation and lesioning provides insights into the functional role of individual nuclei12,13,14,15. The accurate delineation and targeting of the hypothalamus, however, has conventionally been associated with technological and methodological challenges, namely the lack of structural detail and contrast on routinely acquired magnetic resonance imaging (MRI) scans.
To date, an abundance of subcortical atlases has been generated detailing structures commonly targeted during functional neurosurgical procedures such as deep brain stimulation (DBS) surgery. These atlases feature structures such as the thalamus16,17,18, subthalamic nucleus (STN)19,20, and globus pallidus (GP)21. By contrast, the hypothalamus and its surrounding landmarks have remained largely underrepresented. Several groups have accurately delineated the hypothalamus on MRI sequences using manual and semiautomatic segmentation methods8,11,22,23. The majority of studies, however, resorted to identifying the hypothalamus as a single structure. While this approach may suffice for the measurement of overall hypothalamic volumes, the lack of morphological detail prevents more sophisticated analyses at the nuclear level. For clinical applications (e.g., DBS lead localization, stimulation titration, or evaluation of tumor infiltration) and research purposes (e.g. volumetric comparisons of nuclei in physiological and pathological brain states), however, a higher degree of detail is desirable.
The herein employed technique to generate multi-sequence average templates from 990 individual MRI scans is referred to as Minimum deformation averaging (MDA)25. MDA exploits the information contained within inter-individual variations to generate an unbiased, high-resolution, high-contrast population average. Through iterative model building, single-subject data is repeatedly aligned to capture the average morphology of the population used in model generation, yielding a final template of superior imaging quality. The MDA pipeline applied in the generation of our multimodal templates is featured in Fig. 1 and described as follows:
For each subject, MRI datasets were initialized using the minc-bpipe-library pipeline26. An iterative, non-uniform correction of field inhomogeneities was initially performed using the ANTs N4 correction tool26,27. The BEaST brain extraction software was then employed to process the brain masks (BM) and skull strip both T1- and T2-weighted images28. Finally, to achieve a standardized output, the image axis was aligned to MNI152 NLIN 2009b space and the field-of-view was cropped.
The final step of template construction involved the transformation of T1- and T2-weighted templates into MNI space. To this end, the final T1-weighted dataset was non-linearly aligned to an MNI152 NLIN 2009b template using antsRegistration. The same software was then employed to non-linearly register the T2-weighted template to the T1-weighted template.
Spatial relationship between individual hypothalamic nuclei. Hypothalamic nuclei are depicted in a consolidated (top) and an expanded view (bottom) revealing the intrahypothalamic relationships across nuclei. AHA, anterior hypothalamic area; AN, arcuate nucleus; DP, dorsal periventricular nucleus; DM, dorsomedial hypothalamic nucleus; LH, lateral hypothalamus; MPO, medial preoptic nucleus; PA, paraventricular nucleus; PE, periventricular nucleus; PH, posterior hypothalamus; SCh, suprachiasmatic nucleus; SO, supraoptic nucleus; TM, tuberomammillary nucleus; VM, ventromedial nucleus.
The data records are supplied with a lookup table (stored as.csv file), comprising diencephalic structures featuring information about the respective label code, name, and laterality. All files can be opened with standard visualization software such as fsleyes, Display, and freeview. In addition, the data contribution is supplied by volumetric measurements of mean hypothalamic volumes stratified by gender, laterality, and major hypothalamic subdivisions.
The original data used for template construction were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. It is available at -young-adult/document/1200-subjects-data-release.
To determine the reliability of the segmented parcels, the Dice similarity coefficient (DSC) was calculated as a measure of voxel-wise volume overlap across and within raters. To calculate the index, we used the standard definition of DSC, that is specified as the ratio of the intersection volume of two labels, X and Y, to the mean volume of the respective labels37.
A threshold value of 0.35 was defined and only DSCs above this value were deemed appropriate23,34,35. Structures where such a rater agreement could not be achieved were excluded from the final version of the atlas.
Volume estimates of hypothalamic nuclei and evaluation of interrater agreement during manual segmentation. Bar graphs displaying volume estimates for manually segmented hypothalamic nuclei and (a) surrounding gray and white matter structures (b). Volumes of structures that were only partially segmented (e.g. anterior commissure and fornix) are not reported. (c) Spatial overlap between segmented structures and the extent of voxel-wise agreement across raters were calculated using the dice similarity coefficient (DSC) score and (d) Hausdorff distances. Box plots feature the calculated values for hypothalamic nuclei and surrounding (extrahypothalamic) structures within each respective hemisphere. Note that extrahypothalamic structures featured an overall stronger tissue contrast on T1w and T2w images, which ultimately yielded a higher interrater agreement as indicated by a greater DSC score. AH, anterior hypothalamic area; AN, arcuate nucleus; BNST, bed nucleus of stria terminalis; dB, diagonal band of Broca; DP, dorsal periventricular nucleus; DM, dorsomedial hypothalamic nucleus; LH, lateral hypothalamus; MM, mammillary bodies; MPO, medial preoptic nucleus; NBM, nucleus basalis of Meynert; PA, paraventricular nucleus; PE, periventricular nucleus; PH, posterior hypothalamus; RN, red nucleus; SCh, suprachiasmatic nucleus; SN, substantia nigra; SO, supraoptic nucleus; STN, subthalamic nucleus; TM, tuberomammillary nucleus; VM, ventromedial nucleus; ZI, zona incerta.
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