Pattern Recognition in Histopathological Images: An ICPR 2010 Contest

Gürcan, Metin N.; Madabhushi, Anant; Rajpoot, Nasir M.

doi:10.1007/978-3-642-17711-8_23

Cited by 21 publications

(25 citation statements)

References 16 publications

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“…[13] By using these two different datasets, we intend to demonstrate the generality of our proposed framework for detection of cell nuclei of different kinds in histology images. Seven measures were used for quantitative evaluation of performance: three standard measures in sensitivity, positive predictive value (PPV), and F1-score, and four measures used in[13] and denoted as μ d , σ d , μ n , σ n .…”

Section: Resultsmentioning

confidence: 99%

Local isotropic phase symmetry measure for detection of beta cells and lymphocytes

Kuse

Wang

Kalasannavar

et al. 2012

Journal of Pathology Informatics

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Diabetes can be associated with a reduction in functional β cell mass, which must be restored if the disease is to be cured or progress is to be arrested. To study the cell count, it is also necessary to determine the number of nuclei within the insulin stained area. It can take a single experimentalist several months to complete a single study of this kind, results of which may still be quite subjective. In this paper, we propose a framework based on a novel measure of local symmetry for detection of cells. The local isotropic phase symmetry measure (LIPSyM) is designed to give high values at or near the cell centers. We demonstrate the effectiveness of our algorithm for detection of two types of specific cells in histology images, cells in mouse pancreatic sections and lymphocytes in human breast tissue. Experimental results for these two problems show that our algorithm performs better than human experts for the former problem, and outperforms the best reported results for the latter.

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Section: Resultsmentioning

confidence: 99%

Local isotropic phase symmetry measure for detection of beta cells and lymphocytes

Kuse

Wang

Kalasannavar

et al. 2012

Journal of Pathology Informatics

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“…Recently, there has been interest in grand challenge competitions in digital pathology image analysis, in which groups compete with their algorithms on specific problems by using a set of common data sets. These grand challenge competitions have involved detection algorithms to identify nuclei (29), lymphocytes (43), and mitoses (14). In addition to aiding pathologists in manual disease grading (9, 44–47), these identifications are also critical for subsequent automated feature analysis algorithms.…”

Section: Digital Pathology Methodsmentioning

confidence: 99%

Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology

Bhargava

Madabhushi

2016

Annu. Rev. Biomed. Eng.

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125

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Pathology is essential for research in disease and development, as well as for clinical decision making. For more than 100 years, pathology practice has involved analyzing images of stained, thin tissue sections by a trained human using an optical microscope. Technological advances are now driving major changes in this paradigm toward digital pathology (DP). The digital transformation of pathology goes beyond recording, archiving, and retrieving images, providing new computational tools to inform better decision making for precision medicine. First, we discuss some emerging innovations in both computational image analytics and imaging instrumentation in DP. Second, we discuss molecular contrast in pathology. Molecular DP has traditionally been an extension of pathology with molecularly specific dyes. Label-free, spectroscopic images are rapidly emerging as another important information source, and we describe the benefits and potential of this evolution. Third, we describe multimodal DP, which is enabled by computational algorithms and combines the best characteristics of structural and molecular pathology. Finally, we provide examples of application areas in telepathology, education, and precision medicine. We conclude by discussing challenges and emerging opportunities in this area.

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“…Firstly, the ICPR 2010 Histopathology Images contest [4], which consists of 20 images of stained breast cancer tissue. It is required to detect lymphocyte nuclei, while discriminating them from breast cancer nuclei having very similar appearance.…”

Section: Methodsmentioning

confidence: 99%

Learning to Detect Cells Using Non-overlapping Extremal Regions

Arteta

Lempitsky

Noble

et al. 2012

Lecture Notes in Computer Science

146

155

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Abstract. Cell detection in microscopy images is an important step in the automation of cell based-experiments. We propose a machine learning-based cell detection method applicable to different modalities. The method consists of three steps: first, a set of candidate cell-like regions is identified. Then, each candidate region is evaluated using a statistical model of the cell appearance. Finally, dynamic programming picks a set of non-overlapping regions that match the model. The cell model requires few images with simple dot annotation for training and can be learned within a structured SVM framework. In the reported experiments, state-of-the-art cell detection accuracy is achieved for H&E-stained histology, fluorescence, and phase-contrast images.

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Pattern Recognition in Histopathological Images: An ICPR 2010 Contest

Cited by 21 publications

References 16 publications

Local isotropic phase symmetry measure for detection of beta cells and lymphocytes

Local isotropic phase symmetry measure for detection of beta cells and lymphocytes

Emerging Themes in Image Informatics and Molecular Analysis for Digital Pathology

Learning to Detect Cells Using Non-overlapping Extremal Regions

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