Deep learning based cell segmentation in histopathological images
Author
Doğan, Deniz
Advisor
Demir, Çiğdem Gündüz
Date
2018-08Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
204
views
views
287
downloads
downloads
Abstract
In digital pathology, cell imaging systems allow us to comprehend histopathological
events at the cellular level. The first step in these systems is generally cell
segmentation, which substantially affects the subsequent steps for an effective
and reliable analysis of histopathological images. On the other hand, cell segmentation
is a challenging task in histopathological images where there are cells
with different pixel intensities and morphological characteristics. The approaches
that integrate both pixel intensity and morphological characteristics of cells are
likely to achieve successful segmentation results.
This thesis proposes a deep learning based approach for a reliable segmentation
of cells in the images of histopathological tissue samples stained with the
routinely used hematoxylin and eosin technique. This approach introduces two
stage convolutional neural networks that employ pixel intensities in the first stage
and morphological cell features in the second stage. The proposed TwoStageCNN
method is based on extracting cell features, related to cell morphology, from the
class labels and posteriors generated in the first stage and uses the morphological
cell features in the second stage for the final segmentation. We evaluate
the proposed approach on 3428 cells and the experimental results show that our
approach yields better segmentation results compared to different segmentation
techniques.
Keywords
Deep LearningConvolutional Neural Networks
Cell Segmentation
Histopathological Image Analysis