DOI: 10.5061/dryad.0zpc8676p
title: MTL subregion segmentation atlas with 7T and 3T multi-modality MRI

description: Volumetry of subregions in the medial temporal lobe (MTL) computed from
 automatic segmentation in MRI can track neurodegeneration in Alzheimer’s
 disease. However, image quality may vary in MRI. Poor-quality MR images
 can lead to unreliable segmentation of MTL subregions. Considering that
 different MRI contrast mechanisms and field strengths (jointly referred to
 as “modalities” here) offer distinct advantages in imaging different parts
 of the MTL, we developed a multi-modality segmentation model using both 7
 tesla (7T) and 3 tesla (3T) structural MRI to obtain robust segmentation
 in poor-quality images. MRI modalities, including 3T T1-weighted, 3T
 T2-weighted, 7T T1-weighted, and 7T T2-weighted (7T-T2w) of 197
 participants were collected from a longitudinal aging study at the Penn
 Alzheimer’s Disease Research Center. Among them, 7T-T2w was used as the
 primary modality, and all other modalities were rigidly registered to the
 7T-T2w. A model derived from nnU-Net took these registered modalities as
 input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images,
 most of which had high quality, from 25 selected training participants
 were manually segmented to train the multi-modality model. Modality
 augmentation, which randomly replaced certain modalities with Gaussian
 noise, was applied during training to guide the model to extract
 information from all modalities. To compare our proposed model with a
 baseline single-modality model in the full dataset with mixed high/poor
 image quality, we evaluated the ability of derived volume/thickness
 measures to discriminate Amyloid+ mild cognitive impairment (A+MCI) and
 Amyloid- cognitively unimpaired (A-CU) groups, as well as the stability of
 these measurements in longitudinal data. The multi-modality model
 delivered good performance regardless of 7T-T2w quality, while the
 single-modality model under-segmented subregions in poor-quality images.
 The multi-modality model generally demonstrated stronger discrimination of
 A+MCI versus A-CU. Intra-class correlation and Bland-Altman plots
 demonstrate that the multi-modality model had higher longitudinal
 segmentation consistency in all subregions while the single-modality model
 had low consistency in poor-quality images. The multi-modality MRI
 segmentation model provides an improved biomarker for neurodegeneration in
 the MTL that is robust to image quality. It also provides a framework for
 other studies which may benefit from multimodal imaging.
description: # Multi-modality atlas for medial temporal lobe subregion at 7T and 3T MRI
 ## Description of the data and file structure This is a multi-modality
 dataset of medial temporal lobe in brain MRI for the paper about
 [multi-modality subregion segmentation
 model](https://doi.org/10.1002/hipo.70036). 7T-T2w, 7T-T1w, 3T-T2w, and
 3T-T1w data are available at ROI level from 25 anonymized subjects. This
 dataset is included in the zip file "multi_modality_atlas.zip",
 which contains four subfolders, each corresponding to a different step in
 the multi-modality segmentation pipeline. Below is a description of the
 subfolder structure and their contents: 1.
 **`multi_modality_training_atlas`**: Atlas raw data with nnUNet format.
 All subjects were anonymized by being given a fake nnunet ID. Two
 consecutive numbers (e.g., `001` and `002`, `017` and `018`, etc.)
 represent the left and right MTL ROIs, respectively, for the same subject.
 The primary modality is 7T-T2w (channel 0000), and the ROIs of the other
 modalities (7T-T1w inv1 [channel 0001], 7T-T1w inv2 [channel 0002], 3T-T2w
 [channel 0003], and 3T-T1w [channel 0004]) have been registered to the
 7T-T2w ROI for each side. Detailed label correspondence can be found in
 the file `dataset.json`. 2. **`multi_modality_trained_model`**: Trained
 model in nnUNet format. This is for model inference if the user wants to
 run our pipeline on a new test data. 3. **`3tt1_template_roi`**: This
 contains the left and right ROIs in 3T-T1w template space that should be
 used in ROI cropping step. The original template file
 "`template.nii.gz`", which was synthetically generated by
 averaging 29 atlas images, can be downloaded
 from [https://www.nitrc.org/projects/ashs](https://www.nitrc.org/projects/ashs) (ashs_atlas_upennpmc_20170810.tar), the dataset published by paper `Yushkevich, P.A., Pluta, J.B., Wang, H., Xie, L., Ding, S.L., Gertje, E.C., Mancuso, L., Kliot, D., Das, S.R. and Wolk, D.A., 2015. Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Human brain mapping, 36(1), pp.258-287.` 4. **`test_results_on_bnu_dataset`**: The output of running our model on an [independent test set](https://doi.org/10.1038/s41597-025-04586-9). (This dataset is [available](https://plus.figshare.com/articles/dataset/A_paired_dataset_of_multi-modal_MRI_at_3_Tesla_and_7_Tesla_with_manual_hippocampal_subfield_segmentations_on_7T_T2-weighted_images/26075713/1) under the Creative Commons Attribution 4.0 International License (CC BY 4.0).) We acknowledge and credit the original creators as required by the license. Our dataset includes only derived outputs (ROIs and segmentations) and does not redistribute the raw original dataset. ## Code/software The code of this study is available at [https://github.com/liyue3780/mmseg](https://github.com/liyue3780/mmseg)