Booth Id:
CBIO006T
Category:
Computational Biology and Bioinformatics
Year:
2020
Finalist Names:
Kadantsev, Georgii (School: School 564)
Sinitsyn, Aleksandr (School: School 564)
Abstract:
Colorectal cancer is the second highest cause of cancer occurrence and death in men and
women in the United States combined. A specialist usually confirms their diagnosis by a careful
microscopical examination of a tissue sample. As a thorough analysis like this can be quite
difficult when time is of the essence, close attention has been given to developments towards a
computer-based diagnosis system in recent years. In a computer-aided examination of a tissue
sample its digital copy is usually divided into many small images called patches. Each patch is
then analysed individually. In this project we study these histology images using methods from
topological data analysis (TDA). TDA is a modern approach to data analysis which aims to
extract certain topological features from data. The primary characteristic of a patch for us is
persistent entropy of an image, which is extracted from its 0-th persistent homology. It can be
viewed as a certain numerical measure for the chaos present in an image described in a
language of TDA. The goal of this project is to show that the concept of
persistent entropy can be helpful in diagnosis of colorectal cancer. We have developed a fast
and original algorithm for computing 0-th persistent homology and persistent entropy. We have
analysed a big dataset of healthy tissue images and tumor images and observed a significant
difference between the entropy of two patch classes. These findings can become pivotal in a new
computer-based system for colorectal cancer diagnosis.