Details

Autor(en) / Beteiligte

• Abdolhoseini, Mahmoud
• Kluge, Murielle G
• Walker, Frederick R
• Johnson, Sarah J

Titel

Segmentation of Heavily Clustered Nuclei from Histopathological Images

Ist Teil von

• Scientific reports, 2019-03, Vol.9 (1), p.4551-4551, Article 4551

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2019

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Automated 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.