Automatic Segmentation Framework for Fluorescence in Situ Hybridization Cancer Diagnosis

Automatic Segmentation Framework for Fluorescence in Situ Hybridization Cancer Diagnosis

Marcin Stachowiak

Marcin Stachowiak

Wrocław, Województwo dolnośląskie

A method for the cancer diagnosis on the basis of FISH (Fluorescence In Situ Hybridization) test.

Artificial Intelligence

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Description

Algorithms: FISH Pattern recognition, Image processing, Computer aided diagnosis, Breast cancer, Nuclei segmentation, neural networks, SOM, PCA

In this project we address a problem of HER2 and CEN-17 reactions detection in fluorescence in situ hybridization images. These images are very often used in situation where typical biopsy examination is not able to provide enough information to decide on the type of treatment the patient should undergo. Here the main focus is placed on the automatization of the procedure. Using an unsupervised neural network and principal component analysis, we present a segmentation framework that is able to keep the high segmentation accuracy. For comparison purposes we test the neural network approach against an automatic threshold method.

Learn more: https://link.springer.com/chapter/10.1007/978-3-319-45378-1_14

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Marcin S. added photos to project Automatic Segmentation Framework for Fluorescence in Situ Hybridization Cancer Diagnosis

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Automatic Segmentation Framework for Fluorescence in Situ Hybridization Cancer Diagnosis

Algorithms: FISH Pattern recognition, Image processing, Computer aided diagnosis, Breast cancer, Nuclei segmentation, neural networks, SOM, PCA

In this project we address a problem of HER2 and CEN-17 reactions detection in fluorescence in situ hybridization images. These images are very often used in situation where typical biopsy examination is not able to provide enough information to decide on the type of treatment the patient should undergo. Here the main focus is placed on the automatization of the procedure. Using an unsupervised neural network and principal component analysis, we present a segmentation framework that is able to keep the high segmentation accuracy. For comparison purposes we test the neural network approach against an automatic threshold method.

Learn more: https://link.springer.com/chapter/10.1007/978-3-319-45378-1_14

Medium dsc06995 min

Marcin S. created project Automatic Segmentation Framework for Fluorescence in Situ Hybridization Cancer Diagnosis

Medium f52e81c4 ffd1 445f 8bfe 6dd83880a802

Automatic Segmentation Framework for Fluorescence in Situ Hybridization Cancer Diagnosis

Algorithms: FISH Pattern recognition, Image processing, Computer aided diagnosis, Breast cancer, Nuclei segmentation, neural networks, SOM, PCA

In this project we address a problem of HER2 and CEN-17 reactions detection in fluorescence in situ hybridization images. These images are very often used in situation where typical biopsy examination is not able to provide enough information to decide on the type of treatment the patient should undergo. Here the main focus is placed on the automatization of the procedure. Using an unsupervised neural network and principal component analysis, we present a segmentation framework that is able to keep the high segmentation accuracy. For comparison purposes we test the neural network approach against an automatic threshold method.

Learn more: https://link.springer.com/chapter/10.1007/978-3-319-45378-1_14

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