Segmentation of Heavily Clustered Nuclei from Histopathological Images

Scientific Reports - Tập 9 Số 1
Mahmoud Abdolhoseini1, Murielle G. Kluge2, Frederick R. Walker2, Sarah J. Johnson3
1The University of Newcastle, School of Electrical Engineering and Computing, Callaghan, NSW, 2308, Australia
2The University of Newcastle, School of Biomedical Sciences and Pharmacy, Callaghan, NSW, 2308, Australia
3The Hunter Medical Research Institute, New Lambton, NSW, 2305, Australia

Tóm tắt

AbstractAutomated cell nucleus segmentation is the key to gain further insight into cell features and functionality which support computer-aided pathology in early diagnosis of diseases such as breast cancer and brain tumour. Despite considerable advances in automated segmentation, it still remains a challenging task to split heavily clustered nuclei due to intensity variations caused by noise and uneven absorption of stains. To address this problem, we propose a novel method applicable to variety of histopathological images stained for different proteins, with high speed, accuracy and level of automation. Our algorithm is initiated by applying a new locally adaptive thresholding method on watershed regions. Followed by a new splitting technique based on multilevel thresholding and the watershed algorithm to separate clustered nuclei. Finalized by a model-based merging step to eliminate oversegmentation and a model-based correction step to improve segmentation results and eliminate small objects. We have applied our method to three image datasets: breast cancer stained for hematoxylin and eosin (H&E), Drosophila Kc167 cells stained for DNA to label nuclei, and mature neurons stained for NeuN. Evaluated results show our method outperforms the state-of-the-art methods in terms of accuracy, precision, F1-measure, and computational time.

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Tập 9 Số 1

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