An Integrated Environment For Tissue Morphometrics And Analytics

  • Talha Qaiser University of Warwick, Department of Computer Science, Coventry, United Kingdom
  • K. Sirinukunwattana University of Warwick, Department of Computer Science, Coventry, United Kingdom
  • N. Rajpoot University of Warwick, Department of Computer Science, Coventry, United Kingdom


Introduction/ Background

Attaining high reproducibility in cancer diagnosis is still one of the main challenges in modern pathology due to subjectivity [1] [2] [3]. An integrated framework to extract quantitative morphological features from histology images and perform analytics that can lead to the identification of outcome-related features will provide a more accurate and reproducible means to assess cancer [4] [5].


We propose an integrated environment which enables analytics of whole-slide tissue morphometry for a selected cohort of cancer patients. The proposed integrated environment includes three main components (Fig 1): (1) core module comprising of visualization of WSIs at multiple magnification levels, enabling display of clinical and imaging data for multiple cases simultaneously, (2) analytical module contains an interactive tool for measuring dimensions of tissue componentsand interactive annotation module, and (3) analytics module applied to data from a selected subset of cases or all the cases using quantitative morphological measurements including those derived from automatic phenotyping of cells [6]. The proposed environment can be further extended by adding new analytical modules and it can directly bring the benefits of quantitative analysis into pathological practices, thereby increasing reproducibility of cancer diagnosis. Furthermore, it can facilitate the studies of prognostic models, in which morphometric features strongly correlated with the outcome of cancers can be identified and used as image-based markers.


Our integrated environment for tissue analytics consists of core and analytical modules. The core module is a main portal to assess the already available imaging and clinical data, as well as analytical data which   comes   from   integrated   analytical   modules.  These data are interconnected, allowing the users to query imaging data according to available variables in clinical and/or analytical data. It also enables the examination of the clinical and imaging data at the same time. We developed a WSI viewer for exploring the tissue components at different magnification levels by supporting multi-threaded architecture for decompressing the image regions. The state-of-the-art algorithm for automatic phenotyping of all cells in WSIs [6] is the analytic module that makes our interface different from other existing software. The algorithm is capable of identifying multiple classes of cells with high accuracy both in terms of quantitative and quantitative validation. This tool offers a fully automated analysis at the cell population level, in terms of the number and the spatial distribution of different cell types. This can, consequently reduce subjectivity and tediousness of the routine semiquantitative analyses performed by pathologists. This analytical tool is applicable to many prognostic applications such as identifying incidence of metastasis in sentinel lymph nodes, measuring the number of as well as locating tumor-infiltrating lymphocytes, etc.


In this study, we have presented a fully customizable interactive environment for tissue analytics, equipped with measuring, annotating, and automatically cell phenotyping tools. The proposed integrated environment has remarkable potential to assist researchers and pathologists to reduce the human errors (if any) in diagnosing cancers. Further, this environment can serve as a benchmark to develop other morphometric measuring tools.


[1] Smits, A.J.J., et al, The estimation of tumor cell percentage for molecular testing by pathologists is not accurate, Modern Pathology
2014, 27(2):168-174.

[2] Viray, H., et al, A prospective, multi-institutional diagnostic trial to determine pathologist accuracy in estimation of percentage of
malignant cells, Archives of Pathology & Laboratory Medicine 2013, 137(11):1545-1549.

[3] Winters, B., et al, (2012), Diagnostic errors in the intensive care unit: a systematic review of autopsy studies, BMJ Quality & Safety
2012, 21(11):894-902.

[4] Beck, A.H., et al, Systematic analysis of breast cancer morphology uncovers stromal features associated with survival, Science
Translational Medicine 2012, 3(108): 108ra113.

[5] Yuan, Y., et al, Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling, Science
Translational Medicine 2012, 4(157):157ra143.

[6] Sirinukunwattana,K., et al., Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer
Histology Images, IEEE Transactions on Medical Imaging 2016
How to Cite
QAISER, Talha; SIRINUKUNWATTANA, K.; RAJPOOT, N.. An Integrated Environment For Tissue Morphometrics And Analytics. Diagnostic Pathology, [S.l.], v. 1, n. 8, june 2016. ISSN 2364-4893. Available at: <>. Date accessed: 26 may 2019. doi: