Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms

Coelho, Luís Pedro; Shariff, Aabid; Murphy, Robert F.

doi:10.1109/isbi.2009.5193098

Cited by 170 publications

(177 citation statements)

References 11 publications

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“…Fluorescence Microscopy: A hand-segmented set of 97 fluorescence microscopy images with a total of 4009 cells has been published by [31]. For fluorescence microscopy, the simulation of cell population images is an interesting addition to validation with manual labels of domain experts.…”

Section: Public Datasets With Labeling Informationmentioning

confidence: 99%

Computational pathology: Challenges and promises for tissue analysis

Fuchs

Buhmann

2011

Computerized Medical Imaging and Graphics

219

127

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The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.

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Section: Public Datasets With Labeling Informationmentioning

confidence: 99%

Computational pathology: Challenges and promises for tissue analysis

Fuchs

Buhmann

2011

Computerized Medical Imaging and Graphics

219

127

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“…This unique advantage allows the system to develop multiple color and pixel identifiers from digital images of HE stained pancreatic sections of uninjured and injured tissue. Similar technology has recently been used in several other pathology applications, including the identification of tumor cell nuclei in breast and prostate cancer [10,29,30] and apoptosis in cell lines [31].…”

Section: Discussionmentioning

confidence: 99%

A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis

et al. 2014

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The change in exocrine mass is an important parameter to follow in experimental models of pancreatic injury and regeneration. However, at present, the quantitative assessment of exocrine content by histology is tedious and operatordependent, requiring manual assessment of acinar area on serial pancreatic sections. In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. The algorithm works by learning differences in pixel characteristics from input examples provided by human experts. HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the ''ground truth''). After the expert defined the ground truth, the computer was able to develop a prediction rule that was then applied to a unique set of high-resolution images in order to validate the process. For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue area with only a difference of 1%60.05% (p = 0.21). Within regions of injured tissue, the software reported a difference of 2.5%60.04% in acinar area compared with the pathologist (p = 0.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas.

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“…To show the improvement over the standard stochastic watershed provided by the ideas presented in this paper, we apply the method to a database of images with hand-drawn ground-truth segmentation (Coelho et al, 2009). Of the two different collections in this database, we picked the first one, containing 48 images of U2OS cells (one of which was unreadable, leaving us with 47 images), as in Figure 7.…”

Section: Example Application: Fluorescent Microscope Images Of Nucleimentioning

confidence: 99%

Improving the stochastic watershed

Bernander

Gustavsson

Selig

et al. 2013

Pattern Recognition Letters

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The stochastic watershed is an unsupervised segmentation tool recently proposed by Angulo and Jeulin. By repeated application of the seeded watershed with randomly placed markers, a probability density function for object boundaries is created. In a second step, the algorithm then generates a meaningful segmentation of the image using this probability density function. The method performs best when the image contains regions of similar size, since it tends to break up larger regions and merge smaller ones. We propose two simple modifications that greatly improve the properties of the stochastic watershed: (1) add noise to the input image at every iteration, and (2) distribute the markers using a randomly placed grid. The noise strength is a new parameter to be set, but the output of the algorithm is not very sensitive to this value. In return, the output becomes less sensitive to the two parameters of the standard algorithm. The improved algorithm does not break up larger regions, effectively making the algorithm useful for a larger class of segmentation problems.

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Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms

Cited by 170 publications

References 11 publications

Computational pathology: Challenges and promises for tissue analysis

Computational pathology: Challenges and promises for tissue analysis

A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis

Improving the stochastic watershed

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