Nuclei Detection and Segmentation of Histopathological Images Using a Feature Pyramidal Network Variant of a Mask R-CNN
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...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
1 October 2024
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| In: |
Bioengineering
Year: 2024, Volume: 11, Issue: 10, Pages: 1-13 |
| ISSN: | 2306-5354 |
| DOI: | 10.3390/bioengineering11100994 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/bioengineering11100994 Verlag, kostenfrei, Volltext: https://www.mdpi.com/2306-5354/11/10/994 
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| Author Notes: | 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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| 520 | |a 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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