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Machine Learning Based Classification of Colorectal Cancer Tumour Tissue in Whole-Slide Images
Volume 29, Issue 1 (2018), pp. 75–90
Pub. online: 1 January 2018      Type: Research Article      Open accessOpen Access
Received
1 September 2017
Accepted
1 February 2018
Published
1 January 2018

Abstract

The recent introduction of whole-slide scanning systems enabled accumulation of high-quality pathology images into large collections, thus opening new perspectives in cancer research, as well as new analysis challenges. Automated identification of tumour tissue in the whole-slide image enables further use of developed grading systems that classify tumour cell abnormalities and predict tumour developments. In this article, we describe several possibilities to achieve epithelium-stroma classification of tumour tissues in digital pathology images by employing annotated superpixels to train machine learning algorithms. We emphasize that annotating superpixels rather than manually outlining tissue classes in raw images is less time consuming, and more effective way of producing ground truth for computational pathology pipelines. In our approach feature space for supervised learning is created from tissue class assigned superpixels by extracting colour and texture parameters, and applying dimensionality reduction methods. Alternatively, to train convolutional neural network, labelled superpixels are used to generate square image patches by moving fixed size window around each superpixel centroid. The proposed method simplifies the process of ground truth data collection and should minimize the time spent by a skilled expert to perform manual annotation of whole-slide images. We evaluate our method on a private data set of colorectal cancer images. Obtained results confirm that a method produces accurate reference data suitable for the use of different machine learning based classification algorithms.

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Biographies

Morkūnas Mindaugas
mindaugas.morkunas@mii.vu.lt

M. Morkūnas graduated from the Vilnius Gediminas Technical University, Lithuania, in 2002. In 2016 he started PhD studies in informatics engineering at the Institute of Data Science and Digital Technologies, Vilnius University, Lithuania. His interests include bioinformatics, cancer biology, image analysis, machine learning, artificial neural networks.

Treigys Povilas
povilas.treigys@mii.vu.lt

P. Treigys graduated from the Vilnius Gediminas Technical University, Lithuania, in 2005. In 2010 he received the doctoral degree in computer science (PhD) from Institute of Mathematics and Informatics jointly with Vilnius Gediminas Technical University. He is a member of the Lithuanian Society for biomedical engineering. His interests include image analysis, detection and object’s feature extraction in image processing, automated image objects segmentation, optimization methods, artificial neural networks, and software engineering.

Bernatavičienė Jolita
jolita.bernataviciene@mii.vu.lt

J. Bernatavičienė graduated from the Vilnius Pedagogical University in 2004 and received a master’s degree in informatics. In 2008, she received the doctoral degree in computer science (PhD) from Institute of Mathematics and Informatics jointly with Vilnius Gediminas Technical University. She is a researcher at the Cognitive Computing Group of Vilnius University, Institute of Data Science and Digital Technologies. Her research interests include databases, data mining, neural networks, image analysis, visualization, decision support systems and Internet technologies.

Laurinavičius Arvydas
arvydas.laurinavicius@vpc.lt

A. Laurinavičius, MD, PhD fulltime professor at Vilnius University, Department of Pathology, Forensic Medicine and Pharmacology. Director and consultant pathologist at National Center of Pathology. Chair, and board member of multiple international professional societies. Fields of interest: renal pathology, digital pathology image analysis, pathology informatics, health information systems, standards, testing of cancer biomarkers in tissue, multi-resolution analysis of biomarkers.

Korvel Gražina
grazina.korvel@mii.vu.lt

G. Korvel received her BS degree in mathematics and MS degree in informatics (with honors) from Lithuanian University of Educational Sciences, in 2007 and 2009, respectively. She received the doctoral degree from Vilnius University Institute of Data Science and Digital Technologies (former Institute of Mathematics and Informatics) in 2013. Currently she works in this institution. Her research interests include speech signal processing, developing of mathematical models, applications of soft computing and computational intelligence.


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Copyright
© 2018 Vilnius University
Open access article under the CC BY license.

Keywords
tumour whole-slide image machine learning superpixel ground truth colour and texture features convolutional neural network

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