Ramakrishnan, Vignesh and Artinger, Annalena and Barragan, Laura Alexandra Daza and Daza, Jimmy and Winter, Lina and Niedermair, Tanja and Itzel, Timo and Arbelaez, Pablo and Teufel, Andreas and Cotarelo, Cristina L. and Brochhausen, Christoph (2024) Nuclei Detection and Segmentation of Histopathological Images Using a Feature Pyramidal Network Variant of a Mask R-CNN. BIOENGINEERING-BASEL, 11 (10): 994. ISSN 2306-5354
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | digital pathology; Mask R-CNN; nuclei detection; artificial intelligence; histopathology |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Pathologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 26 Nov 2025 08:40 |
| Last Modified: | 26 Nov 2025 08:40 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64403 |
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