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Author: Turkki, Riku
Contributor: University of Helsinki, Faculty of Medicine
Doctoral Programme in Biomedicin
Suomen molekyylilääketieteen instituutti, Helsinki Institute of Life Science
Publisher: Helsingin yliopisto
Date: 2018-08-24
Thesis level: Doctoral dissertation (article-based)
Abstract: The aim of this dissertation was to investigate the use of computer vision for tissue characterization and patient outcome prediction in cancer. This work focused on analysis of digitized tissue specimens, which were stained only for basic morphology (i.e. hematoxylin and eosin). The applicability of texture analysis and convolutional neural networks was evaluated for detection of biologically and clinically relevant features. Moreover, novel approaches to guide ground-truth annotation and outcome-supervised learning for prediction of patient survival directly from the tumor tissue images without expert guidance was investigated. We first studied quantification of tumor viability through segmentation of necrotic and viable tissue compartments. We developed a regional texture analysis method, which was trained and tested on whole sections of mouse xenograft models of human lung cancer. Our experiments showed that the proposed segmentation was able to discriminate between viable and non-viable tissue regions with high accuracy when compared to human expert assessment. We next investigated the feasibility of pre-trained convolutional neural networks in analysis of breast cancer tissue, aiming to quantify tumor-infiltrating lymphocytes in the specimens. Interestingly, our results showed that pre-trained convolutional neural networks can be adapted for analysis of histological image data, outperforming texture analysis. The results also indicated that the computerized assessment was on par with pathologist assessments. Moreover, the study presented an image annotation technique guided by specific antibody staining for improved ground-truth labeling. Direct outcome prediction in breast cancer was then studied using a nationwide patient cohort. A computerized pipeline, which incorporated orderless feature aggregation and convolutional image descriptors for outcome-supervised classification, resulted in a risk grouping that was predictive of both disease-specific and overall survival. Surprisingly, further analysis suggested that the computerized risk prediction was also an independent prognostic factor that provided information complementary to the standard clinicopathological factors. This doctoral thesis demonstrated how computer-vision methods can be powerful tools in analysis of cancer tissue samples, highlighting strategies for supervised characterization of tissue entities and an approach for identification of novel prognostic morphological features.Kudosnäytteiden mikroskooppisten piirteiden visuaalinen tarkastelu on yksi tärkeimmistä määrityksistä syöpäpotilaiden diagnosoinnissa ja hoidon suunnittelussa. Edistyneet kuvantamisteknologiat ovat mahdollistaneet histologisten kasvainkudosnäytteiden digitalisoinnin tarkalla resoluutiolla. Näytteiden digitalisoinnin seurauksena niiden analysointiin voidaan soveltaa edistyneitä koneoppimiseen perustuvia konenäön menetelmiä. Tämä väitöskirja tutkii konenäön menetelmien soveltamista syöpäkudosnäytteiden laskennalliseen analyysiin. Työssä tutkitaan yksittäisten histologisten entiteettien, kuten nekroottisen kudoksen ja immuunisolujen automaattista kvantifiointia. Lisäksi työssä esitellään menetelmä potilaan selviytymisen ennustamiseen pelkkään kudosmorfologiaan perustuen.
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