EBHI-Seg：一种用于图像分割任务的新型肠镜活检组织学苏木精和伊红图像数据集
EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks
摘要：
背景和目的：结直肠癌是一种常见的致命性恶性肿瘤，是全球男性第四大常见癌症，女性第三大常见癌症。在早期阶段及时发现癌症对于治疗疾病至关重要。目前，缺乏用于直肠癌组织病理图像分割的数据集，这往往会影响计算机技术辅助诊断时评估的准确性。研究方法：本研究提供了一个新的公开可用的肠镜活检组织病理学苏木精和伊红图像数据集，用于图像分割任务（EBHI-Seg）。为了证明EBHI-Seg的有效性和广泛性，使用经典机器学习方法和深度学习方法对EBHI-Seg的实验结果进行了评估。结果：实验结果表明，使用EBHI-Seg时，深度学习方法具有更好的图像分割性能。经典机器学习方法的Dice评估指标的最大准确度为0.948，而深度学习方法的Dice评估指标为0.965。结论：该公开数据集包含六种类型肿瘤分化阶段的5，170张图像和相应的地面实况图像。该数据集可以为研究人员提供用于结直肠癌医学诊断的新分割算法，这些算法可以用于临床环境中，以帮助医生和患者。
英文摘要：
Background and Purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of rectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. Results: The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion: This publicly available dataset contained 5,170 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients.
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