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| Verfasst von: | Ramakrishnan, Vignesh [VerfasserIn]  |
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| | Artinger, Annalena [VerfasserIn] |
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| | Daza Barragan, Laura Alexandra [VerfasserIn] |
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| | Daza Barragán, Jimmy Andres [VerfasserIn] |
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| | Winter, Lina [VerfasserIn] |
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| | Niedermair, Tanja [VerfasserIn] |
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| | Itzel, Timo [VerfasserIn] |
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| | Arbelaez, Pablo [VerfasserIn] |
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| | Teufel, Andreas [VerfasserIn] |
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| | López-Cotarelo Rodríguez-Noriega, Cristina [VerfasserIn] |
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| | Brochhausen, Christoph [VerfasserIn] |
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| Titel: | Nuclei Detection and Segmentation of Histopathological Images Using a Feature Pyramidal Network Variant of a Mask R-CNN |
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| Verf.angabe: | Vignesh Ramakrishnan, Annalena Artinger, Laura Alexandra Daza Barragan, Jimmy Daza, Lina Winter, Tanja Niedermair, Timo Itzel, Pablo Arbelaez, Andreas Teufel, Cristina L. Cotarelo and Christoph Brochhausen |
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| E-Jahr: | 2024 |
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| Jahr: | 1 October 2024 |
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| Umfang: | 13 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 02.04.2025 |
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| Titel Quelle: | Enthalten in: Bioengineering |
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| Ort Quelle: | Basel : MDPI, 2014 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 11(2024), 10, Artikel-ID 994, Seite 1-13 |
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| ISSN Quelle: | 2306-5354 |
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| Abstract: | Cell nuclei interpretation is crucial in pathological diagnostics, especially in tumor specimens. A critical step in computational pathology is to detect and analyze individual nuclear properties using segmentation algorithms. Conventionally, a semantic segmentation network is used, where individual nuclear properties are derived after post-processing a segmentation mask. In this study, we focus on showing that an object-detection-based instance segmentation network, the Mask R-CNN, after integrating it with a Feature Pyramidal Network (FPN), gives mature and reliable results for nuclei detection without the need for additional post-processing. The results were analyzed using the Kumar dataset, a public dataset with over 20,000 nuclei annotations from various organs. The dice score of the baseline Mask R-CNN improved from 76% to 83% after integration with an FPN. This was comparable with the 82.6% dice score achieved by modern semantic-segmentation-based networks. Thus, evidence is provided that an end-to-end trainable detection-based instance segmentation algorithm with minimal post-processing steps can reliably be used for the detection and analysis of individual nuclear properties. This represents a relevant task for research and diagnosis in digital pathology, which can improve the automated analysis of histopathological images. |
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| DOI: | doi:10.3390/bioengineering11100994 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.3390/bioengineering11100994 |
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| | kostenfrei: Volltext: https://www.mdpi.com/2306-5354/11/10/994 |
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| | DOI: https://doi.org/10.3390/bioengineering11100994 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | artificial intelligence |
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| | digital pathology |
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| | histopathology |
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| | Mask R-CNN |
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| | nuclei detection |
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| K10plus-PPN: | 1921160535 |
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| Verknüpfungen: | → Zeitschrift |
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Nuclei Detection and Segmentation of Histopathological Images Using a Feature Pyramidal Network Variant of a Mask R-CNN / Ramakrishnan, Vignesh [VerfasserIn]; 1 October 2024 (Online-Ressource)
