DOI: 10.5061/dryad.6djh9w17h
title: Data from: ASHS-OAP atlas for automatic entorhinal cortex segmentation

description: Early stages of Alzheimer’s disease (AD) are associated with volume
 reductions in specific subregions of the medial temporal lobe (MTL). Using
 a manual segmentation method—the Olsen-Amaral-Palombo (OAP) protocol—
 previous work in healthy older adults showed that reductions in grey
 matter volumes in MTL subregions were associated with lower scores on the
 Montreal Cognitive Assessment (MoCA), suggesting atrophy may occur prior
 to diagnosis of mild cognitive impairment, a condition that often
 progresses to AD. However, current manual segmentation methods are labour
 intensive and time consuming. Here, we examined the utility of Automatic
 Segmentation of Hippocampal Subfields (ASHS) to detect volumetric
 differences in MTL subregions of healthy older adults who varied in
 cognitive status as determined by the MoCA. We trained ASHS on the OAP
 protocol to create the ASHS-OAP atlas, and then examined how well
 automated segmentation replicated the ground truth of manual segmentation.
 Volumetric measures obtained from the ASHS-OAP atlas were also contrasted
 against those from the ASHS-PMC atlas, a widely used atlas provided by the
 ASHS team. Volumetrics from the ASHS-OAP atlas aligned well with those
 from manual segmentation, suggesting ASHS-OAP is a viable alternative to
 current manual segmentation methods. In addition, while some subtle
 differences were observed, results from the ASHS-PMC and ASHS-OAP atlases
 aligned well with each other overall. Our findings highlight the utility
 of automated segmentation methods but still underscore the need for a
 unified and harmonized MTL segmentation atlas.
description: Forty community-dwelling healthy older adults (as described in
 Olsen, Yeung et al., 2017; age range = 59-81 years; mean age = 71.4; mean
 education = 16.3 years; range = 12-23; 30 female) were recruited from
 participant databases at the Rotman Research Institute and the University
 of Toronto. Neuroimaging was completed on a 3-Tesla
 Siemens Trio scanner using a 12-channel head coil. All participants
 received a T1-weighted, magnetization-prepared, rapid acquisition with
 gradient echo image (MP-RAGE) whole-brain anatomical scan
 (TE/TR=2.63ms/2000ms, 160 axial slices perpendicular to the AC-PC line,
 voxel size=1x1x1mm, FOV=256mm). The T1-weighted MP-RAGE was used for slice
 placement during the acquisition of a subsequent high in-plane resolution
 T2-weighted scan in an oblique-coronal plane, perpendicular to the
 hippocampal long axis (TE/TR=68/3000ms, 20-28 slices depending on head
 size, 512x512 acquisition matrix, voxel size=0.43x0.43x3mm, no skip,
 FOV=220mm). For the T2-weighted scan, the first slice was placed anterior
 to the collateral sulcus (including the temporal pole where possible) and
 the last slice was placed posterior to the hippocampal tail to ensure full
 coverage of the entire hippocampus and MTL cortices for all
 participants.  A custom atlas was built using the
 Automatic Segmentation of Hippocampal Subfields (ASHS) software
 (Yushkevich et al., 2015) following the published procedures
 (https://sites.google.com/view/ashs-dox/local-ashs/building-an-atlas-for-t2-mri), without the slice heuristics procedure. We generated a custom atlas package (ASHS-OAP) based on the forty healthy older adults’ manual segmentations from Olsen, Yeung et al. (2017). Atlas building in ASHS was performed on a Linux cluster with a Sun Grid Engine. For more details on the OAP segmentation protocol see Olsen et al. (2013), Palombo et al. (2013), and https://github.com/NataliaLadyka/OAPSegmentation/blob/main/Comprehensive%20MTL%20Segmentation%20Protocol%20-%20Updated%20.pdf. The automated segmentation will result in nine regions of interest per hemisphere. These regions are the anterior head and tail of the hippocampus, three subfields of the hippocampus (CA1, CA2/CA3/DG, subiculum) and four medial temporal lobe cortex subregions (anterolateral entorhinal cortex, posteromedial entorhinal cortex, perirhinal cortex, parahippocampal cortex).
description: # ASHS-OAP atlas for automatic entorhinal cortex segmentation
 [10.5061/dryad.6djh9w17h](10.5061/dryad.6djh9w17h∴)
 [ASHS](https://sites.google.com/view/ashs-dox/local-ashs) is an
 open-source tool designed for the Automated Segmentation of Hippocampal
 Subfields from MRI images of the brain. Several atlases for the
 hippocampus are provided with ASHS. While it was developed for the
 segmentation of hippocampal subfields, the tools provided with ASHS can
 potentially be used for multi-atlas segmentation of any region if an
 appropriate set of expert-derived atlas segmentations is provided. Here,
 we extend ASHS by providing an atlas to segment subregions of the adjacent
 entorhinal cortex. ASHS allows users to run the segmentation algorithm
 with a custom atlas (via the `-a ATLASDIR` parameter). Here, we provide
 all of the files required for ASHS to operate on the entorhial cortex.
 These were derived from expert manual segmentation of the regions and by
 using ASHS to create and cross-validate all the required files (see
 manuscript for details). Users must only install these sets of files in a
 folder and direct ASHS to use this folder as the template to perform
 automatic entorhinal cortex segmentation. Briefly, the provided atlas
 files that ASHS uses include: * `adaboost`: This folder contains files
 that have information produced by the machine learning algorithm
 (adaboost)that ASHS uses for segmentation. * `ashs_atlas_vars.sh`: This
 file contains information about the ASHS version and date. *
 `ashs_system_config.sh`: This file contains default settings for ASHS. *
 `ashs_user_config.sh`: This file contains information for the
 configuration of ASHS. * `snap`: This folder contains a file that is used
 to specify the names of the anatomical labels used in the protocol as well
 as the color/value that should be used when generating figures. This file
 is read by ITK-SNAP. * `template`: This folder has files that contain
 template and reference MRI scans and labels/masks for ASHS to use for
 segmentation. Note that the main template structural `template.nii.gz`
 (and the `template_bet.nii.gz` brain-extracted version) is a
 central-tendency model abstracted from the 40 participants. The various
 `refspace` images are crops (and masks thereof) used by ASHS. Each of
 these files was created by ASHS during its template creation process and
 are required for its multi-atlas segmentation of a new participant. *
 `train`: This folder has 40 sub-folders (one for each participant) that
 contain the MRI scans (T1 and T2), manual segmentations, and processed
 images (co-registered and cropped). Each folder represents one of the 40
 participants that ASHS was trained on to create this atlas. During the
 ASHS atlas creation, each training subject's images are cropped
 ("chunk") and registered to the template using both affine
 (`.mat`) and non-linear ("warp") registration. Each of these
 files was generated by ASHS and is required for its multi-atlas
 segmentation of a new participant. ## Description of the data and file
 structure This is an atlas set that can be used with Automatic
 Segmentation of Hippocampal Subfields (ASHS) software to perform automatic
 segmentation of medial temporal lobe subregions. This atlas set was
 constructed from MRI scans and manual segmentations shared by Olsen, Yeung
 et al. (2017). The atlas contains segmented scans of 40 healthy older
 adults acquired at 3 Tesla. The T2-weighted scans are at 0.43x0.43x3mm3
 resolution. The manual segmentations include nine labels per hemisphere.
 These regions include the anterior head and tail of the hippocampus, three
 subfields of the hippocampus (CA1, CA2/CA3/DG, subiculum) and four medial
 temporal lobe cortex subregions (anterolateral entorhinal cortex,
 posteromedial entorhinal cortex, perirhinal cortex, parahippocampal
 cortex). Please see [the primary ASHS installation
 site](https://sites.google.com/view/ashs-dox/local-ashs/installation) for
 instructions on how to download and install ASHS software and atlas
 package to your local machine. Please see [the primary ASHS documentation]
 ()
 for instructions on how to run ASHS using a provided atlas package. The
 dataset provided here make up a custom atlas package. Use this by simply
 specifying the folder you have extracted this to instead of the
 ASHS-provided atlas when calling the `ashs_main` script (the `-a ATLASDIR`
 parameter) in order to segment new datasets according to the provided
 package. ## Code/Software In addition to the provided package, please see
 [the primary ASHS installation
 site](https://sites.google.com/view/ashs-dox/local-ashs/installation) on
 how to download and install ASHS software on your local machine. A working
 installation of the open-source ASHS is required for this package to be
 used.