HEp-2 Cell Classification Using Shape Index Histograms With Donut-Shaped Spatial Pooling

Larsen, Allan; Vestergaard, Jacob S.; Larsen, Rasmus

doi:10.1109/tmi.2014.2318434

Cited by 65 publications

(30 citation statements)

References 30 publications

(44 reference statements)

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“…The descriptors were automatically extracted from images of every individual and collected in a feature matrix. Three types of descriptors were extracted: a colour/ non-colour ratio, gradient-orientation histograms and shape-index histograms [42][43][44] . Collectively, these capture zeroth-, first-and second-order image structure.…”

Section: Methodsmentioning

confidence: 99%

Reproductive isolation related to mimetic divergence in the poison frog Ranitomeya imitator

2014

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In a mimetic radiation-when a single species evolves to resemble different model speciesmimicry can drive within-species morphological diversification, and, potentially, speciation. While mimetic radiations have occurred in a variety of taxa, their role in speciation remains poorly understood. We study the Peruvian poison frog Ranitomeya imitator, a species that has undergone a mimetic radiation into four distinct morphs. Using a combination of colour-pattern analysis, landscape genetics and mate-choice experiments, we show that a mimetic shift in R. imitator is associated with a narrow phenotypic transition zone, neutral genetic divergence and assortative mating, suggesting that divergent selection to resemble different model species has led to a breakdown in gene flow between these two populations. These results extend the effects of mimicry on speciation into a vertebrate system and characterize an early stage of speciation where reproductive isolation between mimetic morphs is incomplete but evident.

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Section: Methodsmentioning

confidence: 99%

Reproductive isolation related to mimetic divergence in the poison frog Ranitomeya imitator

2014

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“…Many well-known features were applied to this application, such as Scale Invariant Feature Email addresses: qixianbiao@gmail.com (Xianbiao Qi), gyzhao@ee.oulu.fi (Guoying Zhao), jiechen@ee.oulu.fi (Jie Chen), mkp@ee.oulu.fi (Matti Pietikäinen) Transformation (SIFT) [11], Local Binary Pattern (LBP) [12] and Gray Level Co-occurrence Matrix (GLCM) [13]. Meanwhile, there were also some new features proposed for the task, such as Co-occurrence of Adjacent LBP (CoALBP) [14] and Shape Index Histogram (SIH) [15]. The feature encoding is an important stage in the traditional Bag-of-Words (BoW) [16] model.…”

Section: Introductionmentioning

confidence: 99%

HEp-2 cell classification: The role of Gaussian Scale Space Theory as a pre-processing approach

Zhao

Chen

et al. 2016

Pattern Recognition Letters

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Indirect Immunofluorescence Imaging of Human Epithelial Type 2 (HEp-2) cells is an effective way to identify the presence of Anti-Nuclear Antibody (ANA). Most existing works on HEp-2 cell classification mainly focus on feature extraction, feature encoding and classifier design. Very few efforts have been devoted to study the importance of the pre-processing techniques. In this paper, we analyze the importance of the pre-processing, and investigate the role of Gaussian Scale Space (GSS) theory as a pre-processing approach for the HEp-2 cell classification task. We validate the GSS pre-processing under the Local Binary Pattern (LBP) and the Bag-of-Words (BoW) frameworks. Under the BoW framework, the introduced pre-processing approach, using only one Local Orientation Adaptive Descriptor (LOAD), achieved superior performance on the Executable Thematic on Pattern Recognition Techniques for Indirect Immunofluorescence (ET-PRT-IIF) image analysis. Our system, using only one feature, outperformed the winner of the ICPR 2014 contest that combined four types of features. Meanwhile, the proposed pre-processing method is not restricted to this work; it can be generalized to many existing works.

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“…Of late, there has been substantial interest in automating the process of identifying mitotic figures in whole slide pathology images (13). Larsen et al (14) used color intensity histograms, gradient orientation histogram and shape index histograms to identify mitoses. Wang et al (15) proposed to use a convolution neural network (CNN) and a set of handcrafted features combined with a random forest classifier to identify mitotic figures in Whole Slide Images (WSI).…”

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confidence: 99%

A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers

et al. 2017

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The treatment and management of early stage estrogen receptor positive (ER1) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distinguish between the more and less aggressive breast tumors, which is a fundamental criterion for the selection of an appropriate treatment plan, Oncotype DX (ODX) and other gene expression tests are typically employed. While informative, these gene expression tests are expensive, tissue destructive, and require specialized facilities. Bloom-Richardson (BR) grade, the common scheme employed in breast cancer grading, has been shown to be correlated with the Oncotype DX risk score. Unfortunately, studies have also shown that the BR grade determined experiences notable inter-observer variability. One of the constituent categories in BR grading is the mitotic index. The goal of this study was to develop a deep learning (DL) classifier to identify mitotic figures from whole slides images of ER+ breast cancer, the hypothesis being that the number of mitoses identified by the DL classifier would correlate with the corresponding Oncotype DX risk categories. The mitosis detector yielded an average F-score of 0.556 in the AMIDA mitosis dataset using a 6-fold validation setup. For a cohort of 174 whole slide images with early stage ER+ breast cancer for which the corresponding Oncotype DX score was available, the distributions of the number of mitoses identified by the DL classifier was found to be significantly different between the high vs low Oncotype DX risk groups (P < 0.01). Comparisons of other risk groups, using both ODX score and histological grade, were also found to present significantly different automated mitoses distributions. Additionally, a support vector machine classifier trained to separate low/ high Oncotype DX risk categories using the mitotic count determined by the DL classifier yielded a 83.19% classification accuracy.

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HEp-2 Cell Classification Using Shape Index Histograms With Donut-Shaped Spatial Pooling

Cited by 65 publications

References 30 publications

Reproductive isolation related to mimetic divergence in the poison frog Ranitomeya imitator

Reproductive isolation related to mimetic divergence in the poison frog Ranitomeya imitator

HEp-2 cell classification: The role of Gaussian Scale Space Theory as a pre-processing approach

A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers

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