Cell Segmentation Using Level Set Methods with a New Variance Term

Bílková, Zuzana; Soukup, Jindřich; Kučera, Václav

doi:10.1007/978-3-319-41501-7_21

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…Gradient flow tracking (GFT) [24], [25], GC [7]- [9], and level set [26]- [28] are energy minimization-based techniques that are more suitable than ACM-based techniques for multiple cell segmentation. GFT identifies cells by clustering the slope vector using the characteristic that the slope vector becomes very congested around a cell, but in a noisy environment, the slope at the cell boundary is quite small, and the direction is unreliable.…”

Section: Related Workmentioning

confidence: 99%

Morphological Multi-Cell Discrimination for Robust Cell Segmentation

Lee

2020

IEEE Access

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Cell segmentation is challenging owing to the existence of various experimental configurations, cell shapes that cannot be mathematically defined, and ambiguous cell boundaries. We propose a cell segmentation method using cell region discriminator and multi-cell discriminator trained using heterogeneous machine-learning techniques such as logistic regression, expectation-maximization, and support vector machine (SVM). The cell-region discriminator identifies the regions where cells are found in images obtained from microscopes via a secondary logistic regressor, and its features use statistical information as well as the distribution of neighbor intensities. The SVM-based multi-cell discriminator determines whether multiple cells are present in the region detected by the cell-region discriminator and whether the region should be divided using the expectation-maximization algorithm. We suggest features for the boundary sectional area and the least square error for cell surface fitting to train the multi-cell discriminator. Using the features and the SVM, the multi-cell discriminator can be trained without overfitting, even for small training data. During this process, the proposed convex cell surface enhances the clustering performance. In experiments, our method based on two discriminators stably divided connected cells even when the contrast between a cell and the background area was small, and it outperformed state-of-the-art methods in terms of cell detection and segmentation accuracy.

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Section: Related Workmentioning

confidence: 99%

Morphological Multi-Cell Discrimination for Robust Cell Segmentation

Lee

2020

IEEE Access

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A Level Set Method with Dynamic Prior for Cell Image Segmentation

Chen

Sun

Wang

2019

Proceedings of the Third International Symposium on Image Computing and Digital Medicine

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PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma

et al. 2018

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Plasma cell segmentation is the first stage of a computer assisted automated diagnostic tool for multiple myeloma (MM). Owing to large variability in biological cell types, a method for one cell type cannot be applied directly on the other cell types. In this paper, we present PCSeg Tool for plasma cell segmentation from microscopic medical images. These images were captured from bone marrow aspirate slides of patients with MM. PCSeg has a robust pipeline consisting of a pre-processing step, the proposed modified multiphase level set method followed by post-processing steps including the watershed and circular Hough transform to segment clusters of cells of interest and to remove unwanted cells. Our modified level set method utilizes prior information about the probability densities of regions of interest (ROIs) in the color spaces and provides a solution to the minimal-partition problem to segment ROIs in one of the level sets of a two-phase level set formulation. PCSeg tool is tested on a number of microscopic images and provides good segmentation results on single cells as well as efficient segmentation of plasma cell clusters.

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Cell Segmentation Using Level Set Methods with a New Variance Term

Cited by 4 publications

References 7 publications

Morphological Multi-Cell Discrimination for Robust Cell Segmentation

Morphological Multi-Cell Discrimination for Robust Cell Segmentation

A Level Set Method with Dynamic Prior for Cell Image Segmentation

PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma

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