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Unsupervised shape prior modeling for cell segmentation in neuroendocrine tumor | IEEE Conference Publication | IEEE Xplore

Unsupervised shape prior modeling for cell segmentation in neuroendocrine tumor


Abstract:

Automated and accurate cell segmentation provides support for many quantitative analyses on digitized neuroendocrine tumor (NET) images. It is a challenging task due to c...Show More

Abstract:

Automated and accurate cell segmentation provides support for many quantitative analyses on digitized neuroendocrine tumor (NET) images. It is a challenging task due to complex variations of cell characteristics. In this paper, we incorporate unsupervised shape priors into an efficient repulsive deformable model for automated cell segmentation on NET images. Unlike other supervised learning based shape models, which usually require a large number of annotated data for training, the proposed algorithm is an unsupervised approach that applies group similarity to shape constraints to avoid any labor intensive annotation. The algorithm is extensively tested on 51 NET images, and the comparative experiments with the state of the arts demonstrate the superior performance of this method using an unsupervised shape model.
Date of Conference: 16-19 April 2015
Date Added to IEEE Xplore: 23 July 2015
Electronic ISBN:978-1-4799-2374-8

ISSN Information:

PubMed ID: 27924189
Conference Location: Brooklyn, NY, USA

1. Introduction

Neuroendocrine tumor (NET) is one of the most frequent cancers worldwide. Computer-aided image analysis, like Ki67 counting, of digitized specimens could potentially support improved characterization of NET. Efficient and accurate cell segmentation is a prerequisite for many computer-aided quantitative analyses such as morphological feature extraction or cell recognition, which is critical for Ki-67 counting. Many state-of-the-art approaches such as multiple level set [1], supervised learning [2], and semi-supervised classification [3] have been successfully applied to nuclei/cell segmentation on microscopic images.

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References

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