Image segmentations produced by BAMF under the AIMI Annotations initiative

The Imaging Data Commons (IDC)(https://imaging.datacommons.cancer.gov/) [1] connects researchers with publicly available cancer imaging data, often linked with other types of cancer data. Many of the collections have limited annotations due to the expense and effort required to create these manually. The increased capabilities of AI analysis of radiology images provides an opportunity to augment existing IDC collections with new annotation data. To further this goal, we trained several nnUNet [2] based models for a variety of radiology segmentation tasks from public datasets and used them to generate segmentations for IDC collections. To validate the models performance, roughly 10% of the predictions were manually reviewed and corrected by both a board certified radiologist and a medical student (non-expert). Additionally, this non-expert looked at all the ai predictions and rated them on a 5 point Likert scale . This record provides the AI segmentations, Manually corrected segmentations, and Manual scores for the inspected IDC Collection images. This work was done in two stages. Versions 1.x of this record were from the first stage. Versions 2.x added additional records. In the Version 2.x additions, the Likert scores were not reported by the manual reviewers. File Overview brain-mr.zip Segment Description: brain tumor regions: necrosis, edema, enhancing IDC Collection: UPENN-GBM Links: model weights, github breast-fdg-pet-ct.zip Segment Description: FDG-avid lesions in breast from FDG PET/CT scans QIN-Breast IDC Collection: QIN-Breast Links: model weights, github breast-mr.zip Segment Description: Breast, Fibroglandular tissue, structural tumor IDC Collection: duke-breast-cancer-mri Links: model weights, github kidney-ct.zip Segment Description: Kidney, Tumor, and Cysts from contrast enhanced CT scans IDS Collection: TCGA-KIRC, TCGA-KIRP, TCGA-KICH, CPTAC-CCRCC Links: model weights, github liver-ct.zip Segment Description: Liver from CT scans IDC Collection: TCGA-LIHC Links: model weights, github liver2-ct.zip Segment Description: Liver and Lesions from CT scans IDC Collection: HCC-TACE-SEG, COLORECTAL-LIVER-METASTASES Links: model weights, github liver-mr.zip Segment Description: Liver from T1 MRI scans IDC Collection: TCGA-LIHC Links: model weights, github lung-ct.zip Segment Description: Lung and Nodules (3mm-30mm) from CT scans IDC Collections: Anti-PD-1-Lung LUNG-PET-CT-Dx NSCLC Radiogenomics RIDER Lung PET-CT TCGA-LUAD TCGA-LUSC Links: model weights 1, model weights 2, github lung2-ct.zip Improved model version Segment Description: Lung and Nodules (3mm-30mm) from CT scans IDC Collections: QIN-LUNG-CT, SPIE-AAPM Lung CT Challenge Links: model weights, github lung-fdg-pet-ct.zip Segment Description: Lungs and FDG-avid lesions in the lung from FDG PET/CT scans IDC Collections: ACRIN-NSCLC-FDG-PET Anti-PD-1-Lung LUNG-PET-CT-Dx NSCLC Radiogenomics RIDER Lung PET-CT TCGA-LUAD TCGA-LUSC Links: model weights, github prostate-mr.zip Segment Description: Prostate from T2 MRI scans IDC Collection: ProstateX, Prostate-MRI-US-Biopsy Links: model weights, github Likert Score Definition: 5 Strongly Agree - Use-as-is (i.e., clinically acceptable, and could be used for treatment without change) 4 Agree - Minor edits that are not necessary. Stylistic differences, but not clinically important. The current segmentation is acceptable 3 Neither agree nor disagree - Minor edits that are necessary. Minor edits are those that the review judges can be made in less time than starting from scratch or are expected to have minimal effect on treatment outcome 2 Disagree - Major edits. This category indicates that the necessary edit is required to ensure correctness, and sufficiently significant that user would prefer to start from the scratch 1 Strongly disagree - Unusable. This category indicates that the quality of the automatic annotations is so bad that they are unusable. Zip File Folder Structure Each zip file in the collection correlates to a specific segmentation task. The common folder structure is ai-segmentations-dcm This directory contains the AI model predictions in DICOM-SEG format for all analyzed IDC collection files qa-segmentations-dcm This directory contains manual corrected segmentation files, based on the AI prediction, in DICOM-SEG format. Only a fraction, ~10%, of the AI predictions were corrected. Corrections were performed by radiologist (rad*) and non-experts (ne*) qa-results.csv CSV file linking the study/series UIDs with the ai segmentation file, radiologist corrected segmentation file, radiologist ratings of AI performance.影像数据共享平台（Imaging Data Commons, IDC）（https://imaging.datacommons.cancer.gov/）[1] 为研究者对接公开可用的癌症影像数据，此类数据通常与其他类型的癌症相关数据存在关联。由于手动标注需耗费高额成本与大量人力，多数数据集仅包含有限的标注信息。随着放射影像AI分析能力的持续提升，为现有IDC数据集补充全新标注数据迎来了可行契机。为达成这一目标，我们基于公开数据集训练了多款nnUNet模型，用于完成各类放射影像分割任务，并利用这些模型为IDC数据集生成分割结果。 为验证模型性能，我们邀请一名执业放射科医师与一名医学生（非专业标注人员）对约10%的模型预测结果进行人工审核与修正。此外，该非专业人员还对全部AI预测结果进行评级，评级采用5级李克特（Likert）量表。 本数据集包含经审核的IDC数据集影像对应的AI分割结果、人工修正分割结果以及人工评级分数。 本数据集的构建分为两个阶段：1.x版本对应第一阶段的成果，2.x版本则新增了更多数据集记录。在2.x版本的新增内容中，人工评审未提供李克特量表评级分数。 文件概览 brain-mr.zip 分割描述：脑肿瘤区域（坏死区、水肿区、强化区） 所属IDC数据集：UPENN-GBM 相关链接：模型权重、GitHub仓库 breast-fdg-pet-ct.zip 分割描述：FDG PET/CT扫描中乳腺的FDG高摄取病灶 所属IDC数据集：QIN-Breast 相关链接：模型权重、GitHub仓库 breast-mr.zip 分割描述：乳腺、纤维腺体组织以及实体肿瘤 所属IDC数据集：duke-breast-cancer-mri 相关链接：模型权重、GitHub仓库 kidney-ct.zip 分割描述：增强CT扫描中的肾脏、肿瘤与囊肿 所属数据集：IDS Collection: TCGA-KIRC、TCGA-KIRP、TCGA-KICH、CPTAC-CCRCC 相关链接：模型权重、GitHub仓库 liver-ct.zip 分割描述：CT扫描中的肝脏 所属IDC数据集：TCGA-LIHC 相关链接：模型权重、GitHub仓库 liver2-ct.zip 分割描述：CT扫描中的肝脏与病灶 所属IDC数据集：HCC-TACE-SEG、COLORECTAL-LIVER-METASTASES 相关链接：模型权重、GitHub仓库 liver-mr.zip 分割描述：T1 MRI扫描中的肝脏 所属IDC数据集：TCGA-LIHC 相关链接：模型权重、GitHub仓库 lung-ct.zip 分割描述：CT扫描中的肺部与结节（直径3mm~30mm） 所属IDC数据集包括： Anti-PD-1-Lung LUNG-PET-CT-Dx NSCLC Radiogenomics RIDER Lung PET-CT TCGA-LUAD TCGA-LUSC 相关链接：模型权重1、模型权重2、GitHub仓库 lung2-ct.zip 改进版模型 分割描述：CT扫描中的肺部与结节（直径3mm~30mm） 所属IDC数据集包括： QIN-LUNG-CT、SPIE-AAPM Lung CT Challenge 相关链接：模型权重、GitHub仓库 lung-fdg-pet-ct.zip 分割描述：FDG PET/CT扫描中的肺部与肺部FDG高摄取病灶 所属IDC数据集包括： ACRIN-NSCLC-FDG-PET Anti-PD-1-Lung LUNG-PET-CT-Dx NSCLC Radiogenomics RIDER Lung PET-CT TCGA-LUAD TCGA-LUSC 相关链接：模型权重、GitHub仓库 prostate-mr.zip 分割描述：T2 MRI扫描中的前列腺 所属IDC数据集：ProstateX、Prostate-MRI-US-Biopsy 相关链接：模型权重、GitHub仓库 李克特量表评级定义 5分 完全同意 - 可直接使用（即临床可接受，无需修改即可用于临床治疗决策） 4分 同意 - 仅需非必要的细微编辑，仅存在风格差异，无临床重要性，当前分割结果可接受 3分 中立 - 需进行必要的细微编辑，此类编辑所需耗时远低于从头标注，且对治疗结局影响极小 2分 不同意 - 需进行大幅编辑，此类编辑为确保结果正确性所必需，其复杂度足以让使用者更倾向于从头开始标注 1分 完全不同意 - 无法使用，自动标注的质量极差，完全不可用 压缩文件文件夹结构 本数据集中的每个压缩文件均对应一项特定的分割任务，通用文件夹结构如下： ai-segmentations-dcm 该目录存储所有经分析的IDC数据集影像对应的AI模型预测结果，格式为DICOM-SEG qa-segmentations-dcm 该目录存储基于AI预测结果经人工修正后的分割文件，格式同样为DICOM-SEG。仅约10%的AI预测结果被修正，修正工作由放射科医师（标注前缀rad*）与非专业人员（标注前缀ne*）完成 qa-results.csv 该CSV文件将研究/序列UID与AI分割文件、放射科医师修正后的分割文件以及放射科医师对AI性能的评级相关联。