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Grant support

This work was supported by the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869. The Titan V used for this research was donated by the NVIDIA Corporation.

Analysis of institutional authors

Paya-Bosch, ElenaAuthorGarcía, GabrielAuthorColomer, AdrianAuthorNaranjo, ValeryAuthor

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Proceedings Paper

Prostate Gland Segmentation in Histology Images via Residual and Multi-resolution U-NET

Publicated to:Lecture Notes In Computer Science. 12489 1-8 - 2020-01-01 12489(), DOI: 10.1007/978-3-030-62362-3_1

Authors: Silva-Rodriguez, Julio; Paya-Bosch, Elena; Garcia, Gabriel; Colomer, Adrian; Naranjo, Valery

Affiliations

Univ Politecn Valencia, Inst Res & Innovat Bioengn, Valencia, Spain - Author
Univ Politecn Valencia, Inst Transport & Terr, Valencia, Spain - Author

Abstract

Prostate cancer is one of the most prevalent cancers worldwide. One of the key factors in reducing its mortality is based on early detection. The computer-aided diagnosis systems for this task are based on the glandular structural analysis in histology images. Hence, accurate gland detection and segmentation is crucial for a successful prediction. The methodological basis of this work is a prostate gland segmentation based on U-Net convolutional neural network architectures modified with residual and multi-resolution blocks, trained using data augmentation techniques. The residual configuration outperforms in the test subset the previous state-of-the-art approaches in an image-level comparison, reaching an average Dice Index of 0.77.

Keywords

Computer aided analysisComputer aided diagnosisComputer aided diagnosis systemsConvolutional neural networksData augmentationDice indicesDiseasesGland segmentationHistologyHistology imagesImage segmentationNetwork architectureProstate cancerProstate cancersProstate glandsResiduaResidualState-of-the-art approachU-netUrology

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Lecture Notes In Computer Science, Q3 Agency Scopus (SJR), its regional focus and specialization in , give it significant recognition in a specific niche of scientific knowledge at an international level.

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 2025-06-12:

  • WoS: 2
  • Scopus: 8
  • OpenCitations: 7

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 2025-06-12:

  • 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: 6.
  • 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: 6 (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: 2.25.
  • The number of mentions on the social network X (formerly Twitter): 4 (Altmetric).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Silva-Rodríguez, Julio) and Last Author (Naranjo Ornedo, Valeriana).

the author responsible for correspondence tasks has been Silva-Rodríguez, Julio.