  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.
