SynthRAD2023 Grand Challenge dataset: synthetizing computed tomography for radiotherapy

Name: SynthRAD2023 Grand Challenge dataset: synthetizing computed tomography for radiotherapy
Creator: Zenodo
Published: 2023-03-24T12:31:51+08:00

Zenodo2023-03-29 更新2026-05-26 收录

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https://zenodo.org/record/7260704 数据链接链接失效反馈

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DATASET STRUCTURE The dataset can be downloaded from https://doi.org/10.5281/zenodo.7260705 and a detailed description is offered at "synthRAD2023_dataset_description.pdf". The training datasets for Task1 is in Task1.zip, while for Task2 in Task2.zip. After unzipping, each Task is organized according to the following folder structure: Task1.zip/ ├── Task1 ├── brain ├── 1Bxxxx ├── mr.nii.gz ├── ct.nii.gz └── mask.nii.gz ├── ... └── overview ├── 1_brain_train.xlsx ├── 1Bxxxx_train.png └── ... └── pelvis ├── 1Pxxxx ├── mr.nii.gz ├── ct.nii.gz ├── mask.nii.gz ├── ... └── overview ├── 1_pelvis_train.xlsx ├── 1Pxxxx_train.png └── .... Task2.zip/ ├──Task2 ├── brain ├── 2Bxxxx ├── cbct.nii.gz ├── ct.nii.gz └── mask.nii.gz ├── ... └── overview ├── 2_brain_train.xlsx ├── 2Bxxxx_train.png └── ... └── pelvis ├── 2Pxxxx ├── cbct.nii.gz ├── ct.nii.gz ├── mask.nii.gz ├── ... └── overview ├── 2_pelvis_train.xlsx ├── 2Pxxxx_train.png └── .... Each patient folder has a unique name that contains information about the task, anatomy, center and a patient ID. The naming follows the convention below: [Task] [Anatomy] [Center] [PatientID] 1 B A 001 In each patient folder, three files can be found: ct.nii.gz: CT image mr.nii.gz or cbct.nii.gz (depending on the task): CBCT/MR image mask.nii.gz:image containing a binary mask of the dilated patient outline For each task and anatomy, an overview folder is provided which contains the following files: [task]_[anatomy]_train.xlsx: This file contains information about the image acquisition protocol for each patient. [task][anatomy][center][PatientID]_train.png: For each patient a png showing axial, coronal and sagittal slices of CBCT/MR, CT, mask and the difference between CBCT/MR and CT is provided. These images are meant to provide a quick visual overview of the data. DATASET DESCRIPTION This challenge dataset contains imaging data of patients who underwent radiotherapy in the brain or pelvis region. Overall, the population is predominantly adult and no gender restrictions were considered during data collection. For Task 1, the inclusion criteria were the acquisition of a CT and MRI during treatment planning while for task 2, acquisitions of a CT and CBCT, used for patient positioning, were required. Datasets for task 1 and 2 do not necessarily contain the same patients, given the different image acquisitions for the different tasks. Data was collected at 3 Dutch university medical centers: Radboud University Medical Center University Medical Center Utrecht University Medical Center Groningen For anonymization purposes, from here on, institution names are substituted with A, B and C, without specifying which institute each letter refers to. The following number of patients is available in the training set. Training Brain Pelvis Center A Center B Center C Total Center A Center B Center C Total Task 1 60 60 60 180 120 0 60 180 Task 2 60 60 60 180 60 60 60 180 Each subset generally contains equal amounts of patients from each center, except for task 1 brain, where center B had no MR scans available. To compensate for this, center A provided twice the number of patients than in other subsets. Validation Brain Pelvis Center A Center B Center C Total Center A Center B Center C Total Task 1 10 10 10 30 20 0 10 30 Task 2 10 10 10 30 10 10 10 30 Testing Brain Pelvis Center A Center B Center C Total Center A Center B Center C Total Task 1 20 20 20 60 40 0 20 60 Task 2 20 20 20 60 20 20 20 60 In total, for all tasks and anatomies combined, 1080 image pairs (720 training, 120 validation, 240 testing) are available in this dataset. This repository only contains the training data. All images were acquired with the clinically used scanners and imaging protocols of the respective centers and reflect typical images found in clinical routine. As a result, imaging protocols and scanner can vary between patients. A detailed description of the imaging protocol for each image, can be found in spreadsheets that are part of the dataset release (see dataset structure). Data was acquired with the following scanners: Center A: MRI: Philips Ingenia 1.5T/3.0T CT: Philips Brilliance Big Bore or Siemens Biograph20 PET-CT CBCT: Elekta XVI Center B: MRI: Siemens MAGNETOM Aera 1.5T or MAGNETOM Avanto_fit 1.5T CT: Siemens SOMATOM Definition AS CBCT: IBA Proteus+ or Elekta XVI Center C: MRI: Siemens Avanto fit 1.5T or Siemens MAGNETOM Vida fit 3.0T CT: Philips Brilliance Big Bore CBCT: Elekta XVI For task 1, MRIs were acquired with a T1-weighted gradient echo or an inversion prepared - turbo field echo (TFE) sequence and collected along with the corresponding planning CTs for all subjects. The exact acquisition parameters vary between patients and centers. For centers B and C, selected MRIs were acquired with Gadolinium contrast, while the selected MRIs of center A were acquired without contrast. For task 2, the CBCTs used for image-guided radiotherapy ensuring accurate patient position were selected for all subjects along with the corresponding planning CT. The following pre-processing steps were performed on the data: Conversion from dicom to compressed nifti (nii.gz) Rigid registration between CT and MR/CBCT Anonymization (face removal, only for brain patients) Patient outline segmentation (provided as a binary mask) Crop MR/CBCT, CT and mask to remove background and reduce file sizes The code used to preprocess the images can be found at: https://github.com/SynthRAD2023/. Detailed information about the dataset are provided in SynthRAD2023_dataset_description.pdf published here along with the data and will also be submitted to Medical Physics. ETHICAL APPROVAL Each institution received ethical approval from their internal review board/Medical Ethical committee: UMC Utrecht approved not-WMO on 4/03/2022 with number 22/474 entitled: “Synthetizing computed tomography for radiotherapy Grand Challenge (SynthRAD)”. UMC Groningen approved not-WMO on 20/07/2022 with number 202200310 entitled: “Synthesizing computed tomography for radiotherapy - Grand Challenge”. Radboud UMC declared the study not-WMO on 17/10/2022 with number 2022-15950 entitled “Synthetizing computed tomography for radiotherapy Grand Challenge”. CHALLENGE DESIGN The overall challenge design can be found at https://doi.org/10.5281/zenodo.7746020.

数据集结构 本数据集可从 https://doi.org/10.5281/zenodo.7260705 下载，详细说明请参阅“synthRAD2023_dataset_description.pdf”。任务1的训练数据集存放于Task1.zip，任务2的训练数据集存放于Task2.zip。解压后，两项任务的文件夹结构如下： Task1.zip/ ├── Task1 │ ├── brain │ │ ├── 1Bxxxx │ │ │ ├── mr.nii.gz │ │ │ ├── ct.nii.gz │ │ │ └── mask.nii.gz │ │ ├── ... │ │ └── overview │ │ ├── 1_brain_train.xlsx │ │ ├── 1Bxxxx_train.png │ │ └── ... │ └── pelvis │ ├── 1Pxxxx │ │ ├── mr.nii.gz │ │ ├── ct.nii.gz │ │ └── mask.nii.gz │ ├── ... │ └── overview │ ├── 1_pelvis_train.xlsx │ ├── 1Pxxxx_train.png │ └── .... Task2.zip/ ├── Task2 │ ├── brain │ │ ├── 2Bxxxx │ │ │ ├── cbct.nii.gz │ │ │ ├── ct.nii.gz │ │ │ └── mask.nii.gz │ │ ├── ... │ │ └── overview │ │ ├── 2_brain_train.xlsx │ │ ├── 2Bxxxx_train.png │ │ └── ... │ └── pelvis │ ├── 2Pxxxx │ │ ├── cbct.nii.gz │ │ ├── ct.nii.gz │ │ └── mask.nii.gz │ ├── ... │ └── overview │ ├── 2_pelvis_train.xlsx │ ├── 2Pxxxx_train.png │ └── .... 每个患者文件夹的名称唯一，包含任务、解剖部位、中心及患者ID信息，命名规则如下：[任务编号] [解剖部位] [中心代号] [患者ID]，例如1 B A 001。每个患者文件夹内包含3个文件： ct.nii.gz：计算机断层扫描（CT）图像 mr.nii.gz 或 cbct.nii.gz（依任务而定）：磁共振成像（MR）或锥形束计算机断层扫描（CBCT）图像 mask.nii.gz：包含扩张后患者轮廓的二值掩码图像针对每项任务与解剖部位，均提供一个overview文件夹，内含以下文件： [task]_[anatomy]_train.xlsx：该文件包含每位患者的图像采集协议信息。 [task][anatomy][center][PatientID]_train.png：每位患者对应的PNG图像，展示了CBCT/MR、CT、掩码的轴位、冠状位、矢状位切片，以及CBCT/MR与CT的差异图，用于快速可视化浏览数据集。 数据集说明 本挑战赛数据集包含接受过头部或盆腔区域放射治疗的患者影像数据。研究人群以成年患者为主，数据收集过程中未设置性别限制。任务1的纳入标准为治疗计划阶段同时采集计算机断层扫描（CT）与磁共振成像（MR）影像；任务2则要求采集用于患者摆位的计算机断层扫描（CT）与锥形束计算机断层扫描（CBCT）影像。由于两项任务的影像采集需求不同，任务1与任务2的数据集未必包含相同的患者。数据采集自荷兰3所大学医学中心：拉德堡德大学医学中心、乌得勒支大学医学中心、格罗宁根大学医学中心。为实现匿名化，后续将机构名称分别替换为A、B、C，不具体对应至某一机构。训练集的患者数量如下： ### 训练集 | | 头部 | | | | 盆腔 | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | 中心A | 中心B | 中心C | 总计 | 中心A | 中心B | 中心C | 总计 | | 任务1 | 60 | 60 | 60 | 180 | 120 | 0 | 60 | 180 | | 任务2 | 60 | 60 | 60 | 180 | 60 | 60 | 60 | 180 | 除任务1头部组外，各子集的患者数量在各中心间分布均衡。任务1头部组中，中心B无可用的MR扫描影像，因此中心A提供了其他子集两倍的患者数量以弥补缺口。 ### 验证集 | | 头部 | | | | 盆腔 | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | 中心A | 中心B | 中心C | 总计 | 中心A | 中心B | 中心C | 总计 | | 任务1 | 10 | 10 | 10 | 30 | 20 | 0 | 10 | 30 | | 任务2 | 10 | 10 | 10 | 30 | 10 | 10 | 10 | 30 | ### 测试集 | | 头部 | | | | 盆腔 | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | 中心A | 中心B | 中心C | 总计 | 中心A | 中心B | 中心C | 总计 | | 任务1 | 20 | 20 | 20 | 60 | 40 | 0 | 20 | 60 | | 任务2 | 20 | 20 | 20 | 60 | 20 | 20 | 20 | 60 | 综上，所有任务与解剖部位的影像对总计1080对，其中训练集720对、验证集120对、测试集240对。本仓库仅包含训练数据。所有影像均使用各中心临床在用的扫描仪与成像协议采集，符合临床常规影像的典型特征，因此不同患者的成像协议与扫描仪型号可能存在差异。各影像的成像协议详细说明可在数据集发布包中的电子表格中查阅（详见数据集结构部分）。各中心使用的扫描仪型号如下：中心A： MR：飞利浦Ingenia 1.5T/3.0T CT：飞利浦Brilliance Big Bore或西门子Biograph20 PET-CT CBCT：医科达XVI 中心B： MR：西门子MAGNETOM Aera 1.5T或MAGNETOM Avanto_fit 1.5T CT：西门子SOMATOM Definition AS CBCT：IBA Proteus+或医科达XVI 中心C： MR：西门子Avanto fit 1.5T或西门子MAGNETOM Vida fit 3.0T CT：飞利浦Brilliance Big Bore CBCT：医科达XVI 针对任务1，磁共振成像（MR）采用T1加权梯度回波或反转准备-快速场回波（TFE）序列采集，并与对应的计划CT影像配对供所有受试者使用。具体采集参数因患者与中心而异。中心B与C的部分MR影像使用钆对比剂增强，而中心A的MR影像未使用对比剂。针对任务2，所有受试者均选用用于图像引导放射治疗以确保患者摆位精准的CBCT影像，并与对应的计划CT影像配对。本数据集已完成以下预处理步骤： 1. 从医学数字成像和通信（DICOM）格式转换为压缩神经影像信息技术格式（nii.gz） 2. 完成CT与MR/CBCT之间的刚性配准 3. 匿名化处理（移除面部区域，仅针对头部患者） 4. 患者轮廓分割（以二值掩码形式提供） 5. 裁剪MR/CBCT、CT与掩码图像以去除背景并减小文件体积预处理代码可在 https://github.com/SynthRAD2023/ 查阅。本数据集的详细说明可随数据一同发布的“SynthRAD2023_dataset_description.pdf”中获取，该文档也将提交至《Medical Physics》期刊。 伦理批准 各机构均已获得其内部审查委员会/医学伦理委员会的伦理批准： - 乌得勒支大学医学中心于2022年3月4日通过非WMO（荷兰医学研究涉及人体受试者法案）审批，编号22/474，项目名称为“放射治疗计算机断层扫描合成挑战赛（SynthRAD）”。 - 格罗宁根大学医学中心于2022年7月20日通过非WMO审批，编号202200310，项目名称为“放射治疗计算机断层扫描合成挑战赛（SynthRAD）”。 - 拉德堡德大学医学中心于2022年10月17日声明本研究无需WMO审批，编号2022-15950，项目名称为“放射治疗计算机断层扫描合成挑战赛（SynthRAD）”。 挑战赛设计 整体挑战赛设计可从 https://doi.org/10.5281/zenodo.7746020 查阅。

提供机构：

Zenodo

创建时间：

2023-03-24

SynthRAD2023 Grand Challenge dataset: synthetizing computed tomography for radiotherapy

资源简介：

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