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Gland Segmentation with Convolutional Neural Networks : Validity of Stroma Segmentation as a General Approach
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Konvolutionella neurala nätverk för segmentering av körtel : Validitet hos stroma-segmentering som en allmän metod (Swedish)
Abstract [en]

The analysis of glandular morphology within histopathology images is a crucial step in determining the stage of cancer. Manual annotation is a very laborious task. It is time consuming and suffers from the subjectivity of the specialists that label the glands. One of the aims of computational pathology is developing tools to automate gland segmentation. Such an algorithm would improve the efficiency of cancer diag- nosis. This is a complex task as there is a large variability in glandular morphologies and staining techniques. So far, specialised models have given promising results focusing on only one organ. This work investigated the idea of a cross domain ap- proximation. Unlike parenchymae the stroma tissue that lies between the glands is similar throughout all organs in the body. Creating a model able to precisely seg- ment the stroma would pave the way for a cross organ model. It would be able to segment the tissue and therefore give access to gland morphologies of different organs. To address this issue, we investigated different new and former architec- tures such as the MILD-net which is the currently best performing algorithm of the GlaS challenge. New architectures were created based on the promising U shaped network as well as Xception and the ResNet for feature extraction. These networks were trained on colon histopathology images focusing on glands and on the stroma. The comparision of the different results showed that this initial cross domain ap- proximation goes into the right direction and incites for further developments.

Place, publisher, year, edition, pages
2019. , p. 66
Series
TRITA-CBH-GRU ; 2019:004
Keywords [en]
segmentation, deep learning, convolutional neural networks, machine learning, artificial intelligence
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-246134OAI: oai:DiVA.org:kth-246134DiVA, id: diva2:1296028
External cooperation
IBM
Subject / course
Medical Engineering
Educational program
Master of Science - Medical Engineering
Supervisors
Examiners
Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-03-13Bibliographically approved

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CiteExportLink to record
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