Turkish Journal of Electrical Engineering and Computer Sciences
Abstract
Automating nuclei segmentation in Hematoxylin and Eosin (H&E) stained images is crucial for advancing computational pathology. Despite significant research, challenges such as overlapping nuclei, varying scanner resolutions, and diverse nuclear morphologies continue to hinder segmentation accuracy. In this paper, we propose a deep-learning based methodology that integrates multiple data augmentation strategies with a U-Net architecture enhanced by the EfficientNetB7 encoder. To enhance generalization, we train the model using a combined dataset from MoNuSeg2018, CPM-17, and CoNSeP, exposing it to diverse staining techniques and tissue types. We then evaluate its robustness on the unseen CryoNuSeg dataset, which consists of fully annotated frozen H&E stained histological images from 10 human organs, as well as on the test sets of MoNuSeg2018, CPM-17, and CoNSeP. The proposed methodology achieves competitive Aggregated Jaccard Index (AJI) scores of 0.8042, 0.7633, 0.7068, and 0.7118 on MoNuSeg2018, CPM-17, CoNSeP, and CryoNuSeg, respectively, demonstrating its effectiveness in segmenting nuclei across diverse histopathological images.
DOI
10.55730/1300-0632.4132
Keywords
Nuclei segmentation, Computational pathology, U-Net, EfficientNet
First Page
372
Last Page
391
Publisher
The Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
ATLAN, FURKAN and HANÇER, EMRAH
(2025)
"A methodology for nuclei segmentation in H&E images using EfficientNetB7 guided U-Net and multiple data augmentation strategies,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 33:
No.
3, Article 10.
https://doi.org/10.55730/1300-0632.4132
Available at:
https://journals.tubitak.gov.tr/elektrik/vol33/iss3/10
