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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00211382%21RIV14-MSM-21230___
rdf:type
n10:Vysledek skos:Concept
dcterms:description
The analysis of protein-level multigene expression signature maps computed from the fusion of differently stained immunohistochemistry images is an emerging tool in cancer investigation. Creating these maps requires registering sets of histological images, a challenging task due to their large size, the non-linear distortions existing between consecutive sections and to the fact that the images correspond to different histological stains and thus, may have very different appearance. This thesis proposal discusses the registration of differently stained consecutive histological sections together with typical image size of several thousands times several thousands pixels. The main idea is to do the segmentation farst and then run the registration on the segmented images which should be faster (simpler similarity metric to evaluate or e.g. kind of contour registration) and more robust. So far, we have preferred the segmentation and registration of stained histological sections independently. Later on we would like to do both processes simultaneously. The thesis proposal briefly summarises the state-of-the-art methods, mainly focusing on registration. We present our histological images and also synthetic datasets we have designed to simulate the real images. We discuss our existing unsupervised multi-class segmentation method and new similarity metric measure for registration of segmented images. In the end, we discuss future work and propose the future research directions. The analysis of protein-level multigene expression signature maps computed from the fusion of differently stained immunohistochemistry images is an emerging tool in cancer investigation. Creating these maps requires registering sets of histological images, a challenging task due to their large size, the non-linear distortions existing between consecutive sections and to the fact that the images correspond to different histological stains and thus, may have very different appearance. This thesis proposal discusses the registration of differently stained consecutive histological sections together with typical image size of several thousands times several thousands pixels. The main idea is to do the segmentation farst and then run the registration on the segmented images which should be faster (simpler similarity metric to evaluate or e.g. kind of contour registration) and more robust. So far, we have preferred the segmentation and registration of stained histological sections independently. Later on we would like to do both processes simultaneously. The thesis proposal briefly summarises the state-of-the-art methods, mainly focusing on registration. We present our histological images and also synthetic datasets we have designed to simulate the real images. We discuss our existing unsupervised multi-class segmentation method and new similarity metric measure for registration of segmented images. In the end, we discuss future work and propose the future research directions.
dcterms:title
Segmentation and registration of multiple stained histological sections -- {PhD} Thesis Proposal Segmentation and registration of multiple stained histological sections -- {PhD} Thesis Proposal
skos:prefLabel
Segmentation and registration of multiple stained histological sections -- {PhD} Thesis Proposal Segmentation and registration of multiple stained histological sections -- {PhD} Thesis Proposal
skos:notation
RIV/68407700:21230/13:00211382!RIV14-MSM-21230___
n10:predkladatel
n11:orjk%3A21230
n3:aktivita
n6:S n6:P
n3:aktivity
P(GAP202/11/0111), S
n3:dodaniDat
n7:2014
n3:domaciTvurceVysledku
n5:8567212
n3:druhVysledku
n13:O
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
104418
n3:idVysledku
RIV/68407700:21230/13:00211382
n3:jazykVysledku
n12:eng
n3:klicovaSlova
segmentation; registration; stained; histology
n3:klicoveSlovo
n8:histology n8:stained n8:segmentation n8:registration
n3:kontrolniKodProRIV
[E4DB063C613D]
n3:obor
n16:JD
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n15:GAP202%2F11%2F0111
n3:rokUplatneniVysledku
n7:2013
n3:tvurceVysledku
Borovec, Jiří
n14:organizacniJednotka
21230