Toward automatic segmentation and quantification of tumor and stroma in whole-slide images of H&E stained rectal carcinomas

Oscar G.F. Geessink, Alexi Baidoshvili, Gerard Freling, Joost M. Klaase, Cornelis H. Slump, Ferdinand van der Heijden

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    6 Citations (Scopus)
    16 Downloads (Pure)


    Visual estimation of tumor and stroma proportions in microscopy images yields a strong, Tumor-(lymph)Node- Metastasis (TNM) classification-independent predictor for patient survival in colorectal cancer. Therefore, it is also a potent (contra)indicator for adjuvant chemotherapy. However, quantification of tumor and stroma through visual estimation is highly subject to intra- and inter-observer variability. The aim of this study is to develop and clinically validate a method for objective quantification of tumor and stroma in standard hematoxylin and eosin (H and E) stained microscopy slides of rectal carcinomas. A tissue segmentation algorithm, based on supervised machine learning and pixel classification, was developed, trained and validated using histological slides that were prepared from surgically excised rectal carcinomas in patients who had not received neoadjuvant chemotherapy and/or radiotherapy. Whole-slide scanning was performed at 20× magnification. A total of 40 images (4 million pixels each) were extracted from 20 whole-slide images at sites showing various relative proportions of tumor and stroma. Experienced pathologists provided detailed annotations for every extracted image. The performance of the algorithm was evaluated using cross-validation by testing on 1 image at a time while using the other 39 images for training. The total classification error of the algorithm was 9.4% (SD = 3.2%). Compared to visual estimation by pathologists, the algorithm was 7.3 times (P = 0.033) more accurate in quantifying tissues, also showing 60% less variability. Automatic tissue quantification was shown to be both reliable and practicable. We ultimately intend to facilitate refined prognostic stratification of (colo)rectal cancer patients and enable better personalized treatment. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
    Original languageEnglish
    Title of host publicationMedical Imaging 2015: Digital Pathology
    Place of PublicationBellingham, WA
    Number of pages7
    ISBN (Print)9781628415100
    Publication statusPublished - 2015
    EventMedical Imaging 2015: Digital Pathology - Orlando, FL, USA
    Duration: 21 Feb 201526 Feb 2015

    Publication series

    NameProceedings of SPIE
    PublisherSPIE, Society of Photo-Optical Instrumentation Engineers
    ISSN (Print)1605-7422


    ConferenceMedical Imaging 2015: Digital Pathology
    Other21-26 February 2015


    • Machine learning
    • Colorectal cancer
    • Image processing
    • Quantification
    • Tissues
    • Hematoxylin and Eosin


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