构建乳腺癌病理图像中分裂细胞检测分割数据集
MiDeSeC: A Dataset for Mitosis Detection and Segmentation in Breast Cancer Histopathology Images
- 从25名患者切片中选取50个区域，每区1024×1024像素
- 共标注超500个分裂细胞，三分之二用于训练，三分之一用于测试
- 覆盖多种分裂形态，助力乳腺癌智能诊断研究
MiDeSeC数据集基于安卡拉大学医学院病理科的25名患者浸润性乳腺癌（非特殊型）H&E染色切片，采用3D Histech Panoramic p250 Flash-3扫描仪与奥林巴斯BX50显微镜在40倍放大下获取。从玻璃切片中选取50个区域，每个区域大小为1024×1024像素。这些区域中共包含超过500个分裂细胞。其中三分之二区域用于训练，其余三分之一用于测试。由于分裂形态多样，需大规模数据集以覆盖所有情况。
原文摘要 · Abstract (English)
The MiDeSeC dataset is created through H&E stained invasive breast carcinoma, no special type (NST) slides of 25 different patients captured at 40x magnification from the Department of Medical Pathology at Ankara University. The slides have been scanned by 3D Histech Panoramic p250 Flash-3 scanner and Olympus BX50 microscope. As several possible mitosis shapes exist, it is crucial to have a large dataset to cover all the cases. Accordingly, a total of 50 regions is selected from glass slides for 25 patients, each of regions with a size of 1024*1024 pixels. There are more than 500 mitoses in total in these 50 regions. Two-thirds of the regions are reserved for training, the other third for testing.
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