University of Oulu

Hoque, Md. Z., Keskinarkaus, A., Nyberg, P., Xu, H., & Seppänen, T. (2023). Invasion depth estimation of carcinoma cells using adaptive stain normalization to improve epidermis segmentation accuracy. In Computerized Medical Imaging and Graphics (Vol. 108, p. 102276). Elsevier BV.

Invasion depth estimation of carcinoma cells using adaptive stain normalization to improve epidermis segmentation accuracy

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Author: Hoque, Md. Ziaul1,2; Keskinarkaus, Anja1; Nyberg, Pia3,4;
Organizations: 1Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland
2Division of Nephrology and Intelligent Critical Care, Department of Medicine, University of Florida, Gainesville, USA
3Biobank Borealis of Northern Finland, Oulu University Hospital, Finland
4Translational Medicine Research Unit, Medical Research Center Oulu, Faculty of Medicine, University of Oulu, Finland
5Department of Electrical and Computer Engineering, University of Alberta, Canada
6School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 5.9 MB)
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Language: English
Published: Elsevier, 2023
Publish Date: 2023-10-12


Submucosal invasion depth is a significant prognostic factor when assessing lymph node metastasis and cancer itself to plan proper treatment for the patient. Conventionally, oncologists measure the invasion depth by hand which is a laborious, subjective, and time-consuming process. The manual pathological examination by measuring accurate carcinoma cell invasion with considerable inter-observer and intra-observer variations is still challenging. The increasing use of medical imaging and artificial intelligence reveals a significant role in clinical medicine and pathology. In this paper, we propose an approach to study invasive behavior and measure the invasion depth of carcinoma from stained histopathology images. Specifically, our model includes adaptive stain normalization, color decomposition, and morphological reconstruction with adaptive thresholding to separate the epithelium with blue ratio image. Our method splits the image into multiple non-overlapping meaningful segments and successfully finds the homogeneous segments to measure accurate invasion depth. The invasion depths are measured from the inner epithelium edge to outermost pixels of the deepest part of particles in image. We conduct our experiments on skin melanoma tissue samples as well as on organotypic invasion model utilizing myoma tissue and oral squamous cell carcinoma. The performance is experimentally compared to three closely related reference methods and our method provides a superior result in measuring invasion depth. This computational technique will be beneficial for the segmentation of epithelium and other particles for the development of novel computer-aided diagnostic tools in biobank applications.

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Series: Computerized medical imaging and graphics
ISSN: 0895-6111
ISSN-E: 1879-0771
ISSN-L: 0895-6111
Volume: 108
Article number: 102276
DOI: 10.1016/j.compmedimag.2023.102276
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
217 Medical engineering
3122 Cancers
Funding: This research has been financially supported by Academy of Finland 6 Genesis Flagship (Grant 318927) and Academy of Finland Identifying trajectories of healthy aging via integration of birth cohorts and biobank data (Grant 309112). Furthermore, we are grateful to the Tauno Tönningin, Riitta and Jorma J. Takanen, and Syöpäsäätiö Foundations for their invaluable generous encouragement and support.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
309112 (Academy of Finland Funding decision)
Copyright information: © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (