Dataset Information

Warping an atlas derived from serial histology to 5 high-resolution MRIs.

ABSTRACT: Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice's Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.

SUBMITTER: Tullo S

PROVIDER: S-EPMC6007088 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Warping an atlas derived from serial histology to 5 high-resolution MRIs.

Tullo Stephanie S Devenyi Gabriel A GA Patel Raihaan R Park Min Tae M MTM Collins D Louis DL Chakravarty M Mallar MM

Scientific data 20180619

Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use ...[more]

PMID: 29917012

Similar Datasets

Project description:Subcortical nuclei and other deep brain structures are known to play an important role in the regulation of the central and peripheral nervous systems. It can be difficult to identify and delineate many of these nuclei and their finer subdivisions in conventional MRI due to their small size, buried location, and often subtle contrast compared to neighboring tissue. To address this problem, we applied a multi-modal approach in ex vivo non-human primate (NHP) brain that includes high-resolution mean apparent propagator (MAP)-MRI and five different histological stains imaged with high-resolution microscopy in the brain of the same subject. By registering these high-dimensional MRI data to high-resolution histology data, we can map the location, boundaries, subdivisions, and micro-architectural features of subcortical gray matter regions in the macaque monkey brain. At high spatial resolution, diffusion MRI in general, and MAP-MRI in particular, can distinguish a large number of deep brain structures, including the larger and smaller white matter fiber tracts as well as architectonic features within various nuclei. Correlation with histology from the same brain enables a thorough validation of the structures identified with MAP-MRI. Moreover, anatomical details that are evident in images of MAP-MRI parameters are not visible in conventional T1-weighted images. We also derived subcortical template "SC21" from segmented MRI slices in three-dimensions and registered this volume to a previously published anatomical template with cortical parcellation (Reveley et al., 2017; Saleem and Logothetis, 2012), thereby integrating the 3D segmentation of both cortical and subcortical regions into the same volume. This newly updated three-dimensional D99 digital brain atlas (V2.0) is intended for use as a reference standard for macaque neuroanatomical, functional, and connectional imaging studies, involving both cortical and subcortical targets. The SC21 and D99 digital templates are available as volumes and surfaces in standard NIFTI and GIFTI formats.

Project description:PurposeIntegrated MRI and linear accelerator systems (MR-Linacs) provide superior soft tissue contrast, and the capability of adapting radiotherapy plans to changes in daily anatomy. In this dataset, serial MRIs of the abdomen of patients undergoing radiotherapy were collected and the luminal gastro-intestinal tract was segmented to support an online segmentation algorithm competition. This dataset may be further utilized by radiation oncologists, medical physicists, and data scientists to further improve auto segmentation algorithms.Acquisition and validation of methodsSerial 0.35T MRIs from patients who were treated on an MR-Linac for tumors located in the abdomen were collected. The stomach, small intestine and large intestine were manually segmented on all MRIs by a team of annotators under the supervision of a board-certified radiation oncologist. Annotator segmentations were validated on 4 representative abdominal MRIs by comparing to the radiation oncologist's contours using 3D Hausdorff distance and 3D Dice coefficient metrics.Data format and usage notesThe dataset includes 467 de-identified scans and their contours from 107 patients. Each patient underwent 1-5 MRI scans of the abdomen. Most of the scans consisted of 144 axial slices with a pixel resolution of 1.5 × 1.5 × 3 mm, leading to 67,248 total slices in the dataset. Images in DICOM format were converted into Portable Graphics Format (PNG) files. Each Portable Graphics Format (PNG) image file stored a slice of the scan, with pixels recorded in 16 bits to cover the full range of intensity values. DICOM-RT segmentations were converted into Comma-Separated Values (CSV) format. Data including images and the annotations is publicly available at https://www.kaggle.com/ds/3577354.Potential applicationsWhile manual segmentations are subject to bias and inter-observer variability, the dataset has been used for the UW-Madison GI Tract Image Segmentation Challenge hosted by Kaggle and may be used for ongoing segmentation algorithm development and potentially for dose accumulation algorithms.

Dataset Information

Warping an atlas derived from serial histology to 5 high-resolution MRIs.

Publications

Warping an atlas derived from serial histology to 5 high-resolution MRIs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets