Jie Shu1,2, Jingxin Liu3, Yongmei Zhang1, Hao Fu4, Mohammad Ilyas5, Giuseppe Faraci6, Vincenzo Della Mea6, Bozhi Liu3, Guoping Qiu3,7. 1. School of Information Science and Technology, North China University of Technology, Beijing, China. 2. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing, China. 3. Shenzhen Univerisity & Guangdong Key Laboratory for Intelligent Signal Processing, Guangzhou, China. 4. College of Intelligence Science and Technology, National University of Defense Technology, Hunan, China. 5. Faculty of Medicine & Health Sciences, Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, NG7 2UH, UK. 6. Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy. 7. Department of Computer Science, University of Nottingham, Nottingham, NG8 1BB, UK.
Abstract
MOTIVATION: For the diagnosis of cancer, manually counting nuclei on massive histopathological images is tedious and the counting results might vary due to the subjective nature of the operation. RESULTS: This paper presents a new segmentation and counting method for nuclei, which can automatically provide nucleus counting results. This method segments nuclei with detected nuclei seed markers through a modified simple one-pass superpixel segmentation method. Rather than using a single pixel as a seed, we created a superseed for each nucleus to involve more information for improved segmentation results. Nucleus pixels are extracted by a newly proposed fusing method to reduce stain variations and preserve nucleus contour information. By evaluating segmentation results, the proposed method was compared to five existing methods on a dataset with 52 immunohistochemically (IHC) stained images. Our proposed method produced the highest mean F1-score of 0.668. By evaluating the counting results, another dataset with more than 30,000 IHC stained nuclei in 88 images were prepared. The correlation between automatically generated nucleus counting results and manual nucleus counting results was up to R2 = 0.901 (p < 0.001). By evaluating segmentation results of proposed method-based tool, we tested on a 2018 Data Science Bowl (DSB) competition dataset, three users obtained DSB score of 0.331 ± 0.006. AVAILABILITY: The proposed method has been implemented as a plugin tool in ImageJ and the source code can be freely downloaded. SUPPLEMENTARY INFORMATION: Supplementary data are available at https://www.dropbox.com/sh/e7oz4nhp3gekvk4/AAC-xuqg5DUx0H5JdqPApbWTa?dl=0s online.
MOTIVATION: For the diagnosis of cancer, manually counting nuclei on massive histopathological images is tedious and the counting results might vary due to the subjective nature of the operation. RESULTS: This paper presents a new segmentation and counting method for nuclei, which can automatically provide nucleus counting results. This method segments nuclei with detected nuclei seed markers through a modified simple one-pass superpixel segmentation method. Rather than using a single pixel as a seed, we created a superseed for each nucleus to involve more information for improved segmentation results. Nucleus pixels are extracted by a newly proposed fusing method to reduce stain variations and preserve nucleus contour information. By evaluating segmentation results, the proposed method was compared to five existing methods on a dataset with 52 immunohistochemically (IHC) stained images. Our proposed method produced the highest mean F1-score of 0.668. By evaluating the counting results, another dataset with more than 30,000 IHC stained nuclei in 88 images were prepared. The correlation between automatically generated nucleus counting results and manual nucleus counting results was up to R2 = 0.901 (p < 0.001). By evaluating segmentation results of proposed method-based tool, we tested on a 2018 Data Science Bowl (DSB) competition dataset, three users obtained DSB score of 0.331 ± 0.006. AVAILABILITY: The proposed method has been implemented as a plugin tool in ImageJ and the source code can be freely downloaded. SUPPLEMENTARY INFORMATION: Supplementary data are available at https://www.dropbox.com/sh/e7oz4nhp3gekvk4/AAC-xuqg5DUx0H5JdqPApbWTa?dl=0s online.