ORCID Identifier(s)

0000-0002-4274-8696

Graduation Semester and Year

2016

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Junzhou Huang

Abstract

Cancer, the second most dreadful disease causing large scale deaths in humans is characterized by uncontrolled growth of cells in the human body and the ability of those cells to migrate from the original site and spread to distant sites. The major proportion of deaths in cancer is due to improper primary diagnosis that raises the need for Computer Aided Diagnosis (CAD). Digital Pathology is a technique that acts as second set of eyes to radiologists in delivering expert level preliminary diagnosis for cancer patients. Cell segmentation is a challenging step in digital pathology that identifies cell regions from micro-slide images and is fundamental for further process like classifying sub- type of tumors or survival prediction. Current techniques of cell segmentation rely on hand crafted features that are dependable on factors like image intensity, shape features, etc. Such computer vision based approaches have two main drawbacks: 1) these techniques might require several manual parameters to be set for accurate segmentation that puts burden on the radiologists. 2) Techniques based on shape or morphological features cannot be generalized as different types of cancer cells are highly asymmetric and irregular. In this thesis, Convolutional Networks, a supervised learning technique recently gaining attention in the field of machine learning for vision perception tasks is investigated to perform end-to-end automated cell segmentation. Three popular convolutional network models namely U-NET, Seg-Net and FCN are chosen and transformed to accomplish cell segmentation and the results are analyzed. A predicament in applying supervised learning models to cell segmentation is the requirement of huge labeled dataset for training our network models. To surmount the absence of labeled dataset for cancer cell segmentation task, a simple labeling tool called SMILE-Annotate was developed to easily mark and label multiple cells in image patches in lung cancer histopathology images. Also, an open source crowd sourced based labeled dataset for cell segmentation from Beck Labs; Harvard University is used to lay empirical evaluations for automated cell segmentation using convolution network models. The result from experiments indicates Seg-Net to be most effectively performing architecture for cell segmentation and also proves it has scope to generalize between different datasets only with minimum efforts involved.

Keywords

Convolutional neural networks, Cancer cell segmentation, Deep learning, Digital pathology

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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