Fused 3-Stage Image Segmentation for Pleural Effusion Cell Clusters
| Autor: | Hong-Ning Dai, Xuguo Sun, Sike Ma, Hao Wang, Meng Zhao, Shengyong Chen, Fan Shi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0303 health sciences
Fuzzy clustering business.industry Computer science Feature extraction Pattern recognition 02 engineering and technology Image segmentation 03 medical and health sciences Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Cluster (physics) 020201 artificial intelligence & image processing Segmentation Artificial intelligence business F1 score Cluster analysis 030304 developmental biology |
| Zdroj: | ICPR |
| Popis: | The appearance of tumor cell clusters in pleural effusion is usually a vital sign of cancer metastasis. Segmentation, as an indispensable basis, is of crucial importance for diagnosing, chemical treatment, and prognosis in patients. However, accurate segmentation of unstained cell clusters containing more detailed features than the fluorescent staining images remains to be a challenging problem due to the complex background and the unclear boundary. Therefore, in this paper, we propose a fused 3-stage image segmentation algorithm, namely Coarse segmentation-Mapping-Fine segmentation (CMF) to achieve unstained cell clusters from whole slide images. Firstly, we establish a tumor cell cluster dataset consisting of 107 sets of images, with each set containing one unstained image, one stained image, and one ground-truth image. Then, according to the features of the unstained and stained cell clusters, we propose a three-stage segmentation method: 1) Coarse segmentation on stained images to extract suspicious cell regions-Region of Interest (ROI); 2) Mapping this ROI to the corresponding unstained image to get the ROI of the unstained image (UI-ROI); 3) Fine Segmentation using improved automatic fuzzy clustering framework (AFCF) on the UI-ROI to get precise cell cluster boundaries. Experimental results on 107 sets of images demonstrate that the proposed algorithm can achieve better performance on unstained cell clusters with an F1 score of 90.40%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|