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hongweilibran
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motum
DOI
10.12751/g-node.tvzqc5
2
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A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors

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motum
Hongwei Bran Li fa413e954f Upload files to 'derivatives' 6 ヶ月 前
derivatives fa413e954f Upload files to 'derivatives' 6 ヶ月 前
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README.md

MOTUM Dataset

This is a public dataset presented at our manuscript titled "A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors" under review in Scientific Data

Description

We introduce a multi-center, multi-origin brain tumor MRI (MOTUM) imaging dataset obtained from 67 patients: 29 with high-grade gliomas, 20 with lung metastases, 10 with breast metastases, 2 with gastric metastasis, 4 with ovarian metastasis, and 2 with melanoma metastasis. This dataset includes anonymized digital imaging and communications in medicine (DICOM) files alongside processed FLAIR, T1-weighted, contrast-enhanced T1-weighted, T2-weighted sequences images, segmentation masks of two tumor regions, and clinical data. Our data-sharing initiative is to support the benchmarking of automated tumor segmentation, multi-modal machine learning, and disease differentiation of multi-origin brain tumors in a multi-center setting.

Data Organization

Multi-contrast MRI data from individuals are stored with the BIDS format. For example,

./sub-0001/anat/ stores four imaging sequences of Subject 0001 including FLAIR, T1, T2, T1-contrast are stored.

./derivatives/ stores the segmentation masks, radiomics, and meta information of four modalities of all subjects.

./Participants.xlsx stores the meta information of each subject.

datacite.yml
Title A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors
Authors Gong,Zhenyu;Department of Neurosurgery, Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China and Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Munich, Germany
Xu,Tao;Department of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China
Peng,Nan;Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
Cheng,Xing;Department of Spine Surgery, Orthopedics Center of Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
Niu,Chen;PET/CT center, the First Affiliated Hospital of Xi’an Jiaotong University, China
Wiestler,Benedikt;Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum Rechts der Isar, TUM, Germany
Hong,Fan;Department of Neurosurgery, Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
Li,Hongwei Bran;Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA
Description We introduce a multi-center, multi-origin brain tumor MRI (MOTUM) imaging dataset obtained from 67 patients: 29 with high-grade gliomas, 20 with lung metastases, 10 with breast metastases, 2 with gastric metastasis, 4 with ovarian metastasis, and 2 with melanoma metastasis. This dataset includes anonymized digital imaging and communications in medicine (DICOM) files alongside processed FLAIR, T1-weighted, contrast-enhanced T1-weighted, T2-weighted sequences images, segmentation masks of two tumor regions, and clinical data. Our data-sharing initiative is to support the benchmarking of automated tumor segmentation, multi-modal machine learning, and disease differentiation of multi-origin brain tumors in a multi-center setting.
License Creative Commons Attribution 4.0 International Public License (https://creativecommons.org/licenses/by/4.0/)
References Zhenyu Gong, Tao Xu, Nan Peng, Xing Cheng, Chen Niu, Benedikt Wiestler, Fan Hong A, and Hongwei Bran Li (2024), A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors. Scientific Data, under review. [doi:tba] (IsDescribedBy)
Funding
Keywords Neuroimaging
Secondary Brain Tumor
Machine Learning
Resource Type Dataset