Tumor ROIs for: Comprehensive evaluation of cross-cancer generalization in histopathology segmentation models across 21 tumor types

This dataset accompanies the paper "Comprehensive evaluation of cross-cancer generalization in histopathology segmentation models across 21 tumor types." It contains 7,616 tumor tissue regions of interest (ROIs) extracted from whole-slide images (WSIs) across 21 TCGA cancer types. Rectangular tumor regions were annotated in QuPath and exported as JPEG images. The original ROIs were extracted at native scanner resolution (varying across slides) and used at full size for the segmentation experiments described in the paper. For this deposit the images have been downsampled to a uniform resolution of 0.5 µm/px (approximately 20× equivalent magnification) to reduce the total dataset size while preserving sufficient detail for visual inspection and reuse. These ROIs served as the input for evaluating five organ-specific deep learning segmentation models (trained on breast, colon, lung, kidney, and prostate tissue). Contents The dataset is organized as 21 TAR archives, one per TCGA project: TCGA-BLCA (445 ROIs), TCGA-BRCA (1,007), TCGA-CESC (276), TCGA-CHOL (38), TCGA-COADREAD (590), TCGA-ESCA (157), TCGA-HNSC (456), TCGA-KICH (109), TCGA-KIRC (512), TCGA-KIRP (282), TCGA-LIHC (372), TCGA-LUAD (379), TCGA-LUSC (301), TCGA-MESO (84), TCGA-OV (106), TCGA-PAAD (199), TCGA-PRAD (415), TCGA-SKCM (457), TCGA-STAD (374), TCGA-THCA (504), TCGA-UCEC (553) Each archive extracts to a directory named after its TCGA project. Individual files follow the naming convention: {TCGA-ID}_{mpp}_tumor_original.jpg TCGA-ID — TCGA case and slide identifier mpp — spatial resolution in microns per pixel (0.500) tumor_original — indicates an unprocessed tumor tissue ROI Related datasets Evaluation data (scoring results, Dice coefficients, clinical metadata): 10.5281/zenodo.18518811 Code (annotation, inference, scoring, and analysis pipeline): 10.5281/zenodo.18520078 Segmentation masks (model prediction masks for all ROIs): 10.5281/zenodo.18669667本数据集配套于论文《21种肿瘤类型病理图像分割模型的跨癌泛化综合评估》。 本数据集包含从21种癌症基因组图谱（TCGA）的全切片图像（Whole-slide images, WSI）中提取的7616个肿瘤组织感兴趣区域（Region of Interest, ROI）。所有矩形肿瘤区域均通过QuPath软件标注并导出为JPEG图像。原始ROIs以扫描仪原生分辨率（不同玻片分辨率存在差异）提取，并以完整尺寸用于论文所述的分割实验。为缩减数据集总规模，本次上传的图像已统一下采样至0.5 µm/px的分辨率（等效约20倍放大倍率），同时保留了足够细节用于目视检查与二次利用。这些ROIs被用作评估5种器官特异性深度学习分割模型的输入数据，这些模型分别针对乳腺、结肠、肺、肾及前列腺组织进行训练。 数据集内容 本数据集以21个TAR压缩包组织，每个压缩包对应一种TCGA项目： TCGA-BLCA（445个ROIs）、TCGA-BRCA（1007个）、TCGA-CESC（276个）、TCGA-CHOL（38个）、TCGA-COADREAD（590个）、TCGA-ESCA（157个）、TCGA-HNSC（456个）、TCGA-KICH（109个）、TCGA-KIRC（512个）、TCGA-KIRP（282个）、TCGA-LIHC（372个）、TCGA-LUAD（379个）、TCGA-LUSC（301个）、TCGA-MESO（84个）、TCGA-OV（106个）、TCGA-PAAD（199个）、TCGA-PRAD（415个）、TCGA-SKCM（457个）、TCGA-STAD（374个）、TCGA-THCA（504个）、TCGA-UCEC（553个） 每个压缩包解压后将生成以对应TCGA项目命名的目录。单个文件遵循如下命名规范： {TCGA-ID}_{mpp}_tumor_original.jpg 其中，TCGA-ID为TCGA病例与玻片标识符，mpp为每像素空间分辨率（单位：微米，此处取值为0.500），tumor_original表示未经过处理的肿瘤组织ROI。 关联数据集 评估数据（评分结果、戴斯系数、临床元数据）：10.5281/zenodo.18518811 代码（标注、推理、评分与分析流程）：10.5281/zenodo.18520078 分割掩码（所有ROIs的模型预测掩码）：10.5281/zenodo.18669667