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Analysis of institutional authors

Alemany-Fornes, MarcAuthor

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May 8, 2023
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A crowdsourcing semi-automatic image segmentation platform for cell biology

Publicated to: Computers In Biology And Medicine. 130 104204- - 2021-01-09 130(), DOI: 10.1016/j.compbiomed.2020.104204

Authors:

Bafti, Saber Mirzaee; Ang, Chee Siang; Hossain, Md Moinul; Marcelli, Gianluca; Alemany-Fornes, Marc; Tsaousis, Anastasios D
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Affiliations

Univ Kent, Sch Biosci, RAPID Grp, Lab Mol & Evolutionary Parasitol, Canterbury CT2 7NJ, Kent, England - Author
Univ Kent, Sch Engn & Digital Arts, Canterbury CT2 7NZ, Kent, England - Author

Abstract

State-of-the-art computer-vision algorithms rely on big and accurately annotated data, which are expensive, laborious and time-consuming to generate. This task is even more challenging when it comes to microbiological images, because they require specialized expertise for accurate annotation. Previous studies show that crowd sourcing and assistive-annotation tools are two potential solutions to address this challenge. In this work, we have developed a web-based platform to enable crowdsourcing annotation of image data; the platform is powered by a semi-automated assistive tool to support non-expert annotators to improve the annotation efficiency. The behavior of annotators with and without the assistive tool is analyzed, using biological images of different complexity. More specifically, non-experts have been asked to use the platform to annotate microbiological images of gut parasites, which are compared with annotations by experts. A quantitative evaluation is carried out on the results, confirming that the assistive tools can noticeably decrease the non-expert annotation's cost (time, click, interaction, etc.) while preserving or even improving the annotation's quality. The annotation quality of non-experts has been investigated using IoU (intersection over union), precision and recall; based on this analysis we propose some ideas on how to better design similar crowdsourcing and assistive platforms.
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Keywords

AlgorithmsComputational biologyCrowdsourcingImage annotationInstance segmentationObject detectionSemi-auto segmentation

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal COMPUTERS IN BIOLOGY AND MEDICINE due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 6/57, thus managing to position itself as a Q1 (Primer Cuartil), in the category Mathematical & Computational Biology. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-03:

  • WoS: 3
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-03:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 33.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 33 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 11.
  • The number of mentions on the social network X (formerly Twitter): 13 (Altmetric).
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: United Kingdom.

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