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%.
|Title of host publication||Proceedings of ICPR 2020 - 25th International Conference on Pattern Recognition|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|Publication status||Published - 5 May 2021|
|Event||25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy|
Duration: 10 Jan 2021 → 15 Jan 2021
|Name||Proceedings - International Conference on Pattern Recognition|
|Conference||25th International Conference on Pattern Recognition, ICPR 2020|
|Period||10/01/21 → 15/01/21|
Bibliographical noteFunding Information:
This document is the results of the research project funded by the National Science Foundation of China (Grant Nos. 61703304, 61906133 and U1509207), R & D Plan in Key Field of Guangdong Province (Grant No. 2019B010109001), Major Science and Technology Projects of Tianjin (Grant No.18ZXZNGX00150) and is carried out with the support of ERCIM ‘Alain Bensoussan’ Fellowship Programme.
© 2020 IEEE