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Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects

Overview of attention for article published in Journal of Pathology Informatics, September 2017
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Article details
Title
Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects
Published in
Journal of Pathology Informatics, September 2017
DOI 10.4103/jpi.jpi_22_17
Pubmed ID
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Abstract

Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as "probably a mitosis" by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using k-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses' surrounding cells of each image were also extracted. The Kruskal-Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects.

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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 1 25%
Other 1 25%
Unknown 2 50%
Readers by discipline
Readers by discipline Count As %
Computer Science 2 50%
Unknown 2 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 October 2017.
All research outputs
#19,159,670
of 27,788,586 outputs
Outputs from Journal of Pathology Informatics
#294
of 453 outputs
Outputs of similar age
#214,194
of 329,328 outputs
Outputs of similar age from Journal of Pathology Informatics
#10
of 10 outputs
Altmetric has tracked 27,788,586 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 453 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 25th percentile – i.e., 25% of its peers scored the same or lower than it.
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We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one.