Cited by 315AI in cancer detectionMedical Image Segmentation TechniquesCell Image Analysis Techniques

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Structured crowdsourcing enables convolutional segmentation of histology images

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Amgad, Mohamed, Elfandy, Habiba, Hussein, Hagar, Atteya, Lamees A, Elsebaie, Mai A T, Abo Elnasr, Lamia S, Sakr, Rokia A, Salem, Hazem S E, Ismail, Ahmed F, Saad, Anas M, Ahmed, Joumana, Elsebaie, Maha A T, Rahman, Mustafijur, Ruhban, Inas A, Elgazar, Nada M, Alagha, Yahya, Osman, Mohamed H, Alhusseiny, Ahmed M, Khalaf, Mariam M, Younes, Abo-Alela F, Abdulkarim, Ali, Younes, Duaa M, Gadallah, Ahmed M, Elkashash, Ahmad M, Fala, Salma Y, Zaki, Basma M, Beezley, Jonathan, Chittajallu, Deepak R, Manthey, David, Gutman, David A, Cooper, Lee A D·2019·Bioinformatics

DOI 10.1093/bioinformatics/btz083PMC PMC6748796

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Abstract

MOTIVATION: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. RESULTS: We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION: Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Color transfer between images
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Authors: Amgad, M., Elfandy, H., Hussein, H., Atteya, L. A., Elsebaie, M. A. T., Abo Elnasr, L. S., Sakr, R. A., Salem, H. S. E., Ismail, A. F., Saad, A. M., Ahmed, J., Elsebaie, M. A. T., Rahman, M., Ruhban, I. A., Elgazar, N. M., Alagha, Y., Osman, M. H., Alhusseiny, A. M., Khalaf, M. M., Younes, A.-A. F., Abdulkarim, A., Younes, D. M., Gadallah, A. M., Elkashash, A. M., Fala, S. Y., Zaki, B. M., Beezley, J., Chittajallu, D. R., Manthey, D., Gutman, D. A., & Cooper, L. A. D.
Affiliations: Emory University (US)
Department of Pathology, National Cancer Institute , Cairo, Egypt
Cairo University (EG)
Ministry of Health and Population (EG)
Ain Shams University (EG)
Department of Medicine, Menoufia University , Menoufia, Egypt
Menoufia University (EG)
Alexandria University (EG)
University of Chittagong (BD)
Damascus University (SY)
Mansoura University (EG)
Zagazig University (EG)
Batterjee Medical College (SA)
Suez Canal University (EG)
Kitware (United States) (US)
Corresponding author: Cooper, Lee A D
ORCID: Amgad, Mohamed
Elfandy, Habiba
Hussein, Hagar
Elsebaie, Mai A T
Sakr, Rokia A
Salem, Hazem S E
Saad, Anas M
Elsebaie, Maha A T
Rahman, Mustafijur
Osman, Mohamed H
Alhusseiny, Ahmed M
Abdulkarim, Ali
Gadallah, Ahmed M
Chittajallu, Deepak R
Manthey, David
Gutman, David A
Cooper, Lee A D
Venue: Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2019-02-06
Volume: 35
Issue: 18
Pages: 3461-3467
ISSN: 1367-4803 (ISSN-L)
1367-4811
DOI: 10.1093/bioinformatics/btz083
PMC: PMC6748796
OpenAlex: W2922239620
Cited by: 315
References: 11
Citations by year: 2026: 41 · 2025: 73 · 2024: 61 · 2023: 60 · 2022: 42 · 2021: 20 · 2020: 17 · 2019: 1
FWCI: 9.12
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Bioinformatics (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Greater South Information System
Funders: U.S. National Institutes of Health
National Cancer Institute grants (U01CA220401, U24CA194362)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
Keywords: breast cancer, whole slide images, annotated dataset, fully convolutional network
MeSH terms: Algorithms, Breast Neoplasms, Histological Techniques, Humans, Crowdsourcing

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Structured crowdsourcing enables convolutional segmentation of histology images

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