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Papers by Marcin Kociolek

Research paper thumbnail of Survey statistics of automated segmentations applied to optical imaging of mammalian cells

BMC bioinformatics, 2015

The goal of this survey paper is to overview cellular measurements using optical microscopy imagi... more The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging mo...

Research paper thumbnail of A Braille printer on reusable thermoplastic sheets

Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. No.99CH37015), 1999

ABSTRACT Our research is aimed at the development of a thermal Braille printing system on reusabl... more ABSTRACT Our research is aimed at the development of a thermal Braille printing system on reusable memory shape plastic films. Previously embossed dots can be erased by local increase of temperature. The authors present a short overview of a system with simulation and temperature measurements using thermography

Research paper thumbnail of Segmenting time-lapse phase contrast images of adjacent NIH 3T3 cells

Journal of Microscopy, 2013

We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is ... more We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is accurate even when cells are physically in contact with each other. The problem of segmentation, when cells are in contact, poses a challenge to the accurate automation of cell counting, tracking and lineage modelling in cell biology. The segmentation method presented in this paper consists of (1) background reconstruction to obtain noise-free foreground pixels and (2) incorporation of biological insight about dividing and nondividing cells into the segmentation process to achieve reliable separation of foreground pixels defined as pixels associated with individual cells. The segmentation results for a time-lapse image stack were compared against 238 manually segmented images (8219 cells) provided by experts, which we consider as reference data. We chose two metrics to measure the accuracy of segmentation: the 'Adjusted Rand Index' which compares similarities at a pixel level between masks resulting from manual and automated segmentation, and the 'Number of Cells per Field' (NCF) which compares the number of cells identified in the field by manual versus automated analysis. Our results show that the automated segmentation compared to manual segmentation has an average adjusted rand index of 0.96 (1 being a perfect match), with a standard deviation of 0.03, and an average difference of the two numbers of cells per field equal to 5.39% with a standard deviation of 4.6%.

Research paper thumbnail of Comparison of segmentation algorithms for fluorescence microscopy images of cells

Cytometry Part A, 2011

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. ... more The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc. y Key terms fluorescence microscopy; k-means cluster; image segmentation; cell edge; bivariate similarity index

Research paper thumbnail of Discrete Wavelet transform–Derived features for digital Image texture Analysis

Proc. of Interational …, Jan 1, 2001

This paper deals with using discrete wavelet transform derived features used for digital image te... more This paper deals with using discrete wavelet transform derived features used for digital image texture analysis. Wavelets appear to be a suitable tool for this task, because they allow analysis of images at various levels of resolution. The proposed features have been tested on images from standard Brodatz catalogue.

Research paper thumbnail of Survey statistics of automated segmentations applied to optical imaging of mammalian cells

BMC bioinformatics, 2015

The goal of this survey paper is to overview cellular measurements using optical microscopy imagi... more The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging mo...

Research paper thumbnail of A Braille printer on reusable thermoplastic sheets

Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. No.99CH37015), 1999

ABSTRACT Our research is aimed at the development of a thermal Braille printing system on reusabl... more ABSTRACT Our research is aimed at the development of a thermal Braille printing system on reusable memory shape plastic films. Previously embossed dots can be erased by local increase of temperature. The authors present a short overview of a system with simulation and temperature measurements using thermography

Research paper thumbnail of Segmenting time-lapse phase contrast images of adjacent NIH 3T3 cells

Journal of Microscopy, 2013

We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is ... more We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is accurate even when cells are physically in contact with each other. The problem of segmentation, when cells are in contact, poses a challenge to the accurate automation of cell counting, tracking and lineage modelling in cell biology. The segmentation method presented in this paper consists of (1) background reconstruction to obtain noise-free foreground pixels and (2) incorporation of biological insight about dividing and nondividing cells into the segmentation process to achieve reliable separation of foreground pixels defined as pixels associated with individual cells. The segmentation results for a time-lapse image stack were compared against 238 manually segmented images (8219 cells) provided by experts, which we consider as reference data. We chose two metrics to measure the accuracy of segmentation: the 'Adjusted Rand Index' which compares similarities at a pixel level between masks resulting from manual and automated segmentation, and the 'Number of Cells per Field' (NCF) which compares the number of cells identified in the field by manual versus automated analysis. Our results show that the automated segmentation compared to manual segmentation has an average adjusted rand index of 0.96 (1 being a perfect match), with a standard deviation of 0.03, and an average difference of the two numbers of cells per field equal to 5.39% with a standard deviation of 4.6%.

Research paper thumbnail of Comparison of segmentation algorithms for fluorescence microscopy images of cells

Cytometry Part A, 2011

The analysis of fluorescence microscopy of cells often requires the determination of cell edges. ... more The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability. Published 2011 Wiley-Liss, Inc. y Key terms fluorescence microscopy; k-means cluster; image segmentation; cell edge; bivariate similarity index

Research paper thumbnail of Discrete Wavelet transform–Derived features for digital Image texture Analysis

Proc. of Interational …, Jan 1, 2001

This paper deals with using discrete wavelet transform derived features used for digital image te... more This paper deals with using discrete wavelet transform derived features used for digital image texture analysis. Wavelets appear to be a suitable tool for this task, because they allow analysis of images at various levels of resolution. The proposed features have been tested on images from standard Brodatz catalogue.