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Local structure prediction for gland segmentation

Local structure prediction for gland segmentation

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Original languageEnglish
Title of host publication2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
Subtitle of host publicationFrom Nano to Macro, ISBI 2016 - Proceedings
PublisherIEEE
Pages799-802
Number of pages4
Volume2016-June
ISBN (Electronic)9781479923496
ISBN (Print)9781479923502
DOIs
StatePublished - 16 Jun 2016
Event2016 IEEE 13th International Symposium on Biomedical Imaging - Prague, Czech Republic

Conference

Conference2016 IEEE 13th International Symposium on Biomedical Imaging
Abbreviated titleISBI 2016
CountryCzech Republic
CityPrague
Period13/04/1616/04/16
Internet address

Abstract

We present a method to segment individual glands from colon histopathology images. Segmentation based on sliding window classification does not usually make explicit use of information about the spatial configurations of class labels. To improve on this we propose to segment glands using a structure learning approach in which the local label configurations (structures) are considered when training a support vector machine classifier. The proposed method not only distinguishes foreground from background, it also distinguishes between different local structures in pixel labelling, e.g. locations between adjacent glands and locations far from glands. It directly predicts these label configurations at test time. Experiments demonstrate that it produces better segmentations than when the local label structure is not used to train the classifier.

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