EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks
Background and purposeColorectal 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 histopatho...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1114673/full |
| _version_ | 1828055981137854464 |
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| author | Liyu Shi Xiaoyan Li Weiming Hu Haoyuan Chen Jing Chen Zizhen Fan Minghe Gao Yujie Jing Guotao Lu Deguo Ma Zhiyu Ma Qingtao Meng Dechao Tang Hongzan Sun Marcin Grzegorzek Marcin Grzegorzek Shouliang Qi Yueyang Teng Chen Li |
| author_facet | Liyu Shi Xiaoyan Li Weiming Hu Haoyuan Chen Jing Chen Zizhen Fan Minghe Gao Yujie Jing Guotao Lu Deguo Ma Zhiyu Ma Qingtao Meng Dechao Tang Hongzan Sun Marcin Grzegorzek Marcin Grzegorzek Shouliang Qi Yueyang Teng Chen Li |
| author_sort | Liyu Shi |
| collection | DOAJ |
| description | Background and purposeColorectal 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 colorectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis.MethodsThis 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.ResultsThe 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.ConclusionThis publicly available dataset contained 4,456 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. EBHI-Seg is publicly available at: https://figshare.com/articles/dataset/EBHI-SEG/21540159/1. |
| first_indexed | 2024-04-10T20:48:13Z |
| format | Article |
| id | doaj.art-9c96110c25f1483fb5bb8b1ebdfb1bc8 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-10T20:48:13Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-9c96110c25f1483fb5bb8b1ebdfb1bc82023-01-24T05:58:08ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-01-011010.3389/fmed.2023.11146731114673EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasksLiyu Shi0Xiaoyan Li1Weiming Hu2Haoyuan Chen3Jing Chen4Zizhen Fan5Minghe Gao6Yujie Jing7Guotao Lu8Deguo Ma9Zhiyu Ma10Qingtao Meng11Dechao Tang12Hongzan Sun13Marcin Grzegorzek14Marcin Grzegorzek15Shouliang Qi16Yueyang Teng17Chen Li18Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaDepartment of Pathology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shengyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaShengjing Hospital, China Medical University, Shenyang, ChinaInstitute of Medical Informatics, University of Lübeck, Lübeck, GermanyDepartment of Knowledge Engineering, University of Economics in Katowice, Katowice, PolandMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaMicroscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaBackground and purposeColorectal 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 colorectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis.MethodsThis 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.ResultsThe 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.ConclusionThis publicly available dataset contained 4,456 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. EBHI-Seg is publicly available at: https://figshare.com/articles/dataset/EBHI-SEG/21540159/1.https://www.frontiersin.org/articles/10.3389/fmed.2023.1114673/fullcolorectal histopathologyenteroscope biopsyimage datasetimage segmentationEBHI-Seg |
| spellingShingle | Liyu Shi Xiaoyan Li Weiming Hu Haoyuan Chen Jing Chen Zizhen Fan Minghe Gao Yujie Jing Guotao Lu Deguo Ma Zhiyu Ma Qingtao Meng Dechao Tang Hongzan Sun Marcin Grzegorzek Marcin Grzegorzek Shouliang Qi Yueyang Teng Chen Li EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks Frontiers in Medicine colorectal histopathology enteroscope biopsy image dataset image segmentation EBHI-Seg |
| title | EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks |
| title_full | EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks |
| title_fullStr | EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks |
| title_full_unstemmed | EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks |
| title_short | EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks |
| title_sort | ebhi seg a novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks |
| topic | colorectal histopathology enteroscope biopsy image dataset image segmentation EBHI-Seg |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1114673/full |
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