Jie Shu
ABSTRACT
The analysis of nucleus shape is useful for the pathological diagnosis or prognosis. The shape of the nucleus in the digital pathological images from different sources may vary greatly. Although, Convolutional Neural Network (CNN) has proven its success in automatic nucleus segmentation, segmentation performance may be reduced due to varies in nucleus size. In this paper, we proposed a CNN based size adaptive nucleus segmentation method. This method adapts the CNN model to image clusters with similar estimated nucleus size to increase its nucleus segmentation performance. Furthermore our method can automatically segment nuclei and the only parameter that needs to be set is the number of clusters. We compared this method to several existing methods on two datasets from the nucleus segmentation challenge. The proposed method achieved satisfactory results with mean Intersection-over-Union (IoU) 0.718 and 0.798 and mean F1-score 0.780 and 0.864. In addition, compared with the method without size adaptation, the proposed method improves the segmentation performance and is easy to implement.
- Ronneberger O, Fischer P, and Brox T. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Munich, GER, 234-241. https://doi.org/10.1007/978-3-319-24574-4_28Google Scholar
- He KM, Gkioxari G, Dollar P and Girshick R. 2017. Mask R-CNN. In Proceedings of the 16th IEEE International Conference on Computer Vision (ICCV). Venice, IT, 2980-2988. https://doi.org/10.1109/ICCV.2017.322Google Scholar
- Sommer C, Straehle C, Kothe U, 2011. Ilastik: Interactive learning and segmentation toolkit. In Proceedings of the 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro. Chicago, IL, 230-233.Google ScholarCross Ref
- Jie S, Jingxin L, Yongmei Z, 2020. Marker controlled superpixel nuclei segmentation and automatic counting on immunohistochemistry staining images. Bioinformatics, 36, 10 (May 2020), 3225-3233. https://doi.org/10.1093/bioinformatics/btaa107Google Scholar
- Hollandi R, 2019. A deep learning framework for nucleus segmentation using image style transfer, https://doi.org/10.1109/ICCV.2017.322 10.1101/580605.Google Scholar
- Hartigan J A and Wong M A. 1979. A K-Means Clustering Algorithm. Applied Statistics, 28, 1 (1979), https://doi.org/10.1093/bioinformatics/btaa107Google Scholar
- Caicedo J C, Goodman A, Karhohs K W, 2019. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nature Methods, 16, 12 (DEC 2019), 1247-1253. https://doi.org/10.1109/ICCV.2017.322Google ScholarCross Ref
- Caicedo JC, Roth J, Goodman A, 2019. Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images. Wiley, 95, 9 (JUL 2019), 952-965. https://doi.org/10.1093/bioinformatics/btaa107Google Scholar
- Li G, Liu TM, Nie JX, 2007. Segmentation of touching cells using gradient flow tracking. In the Proceedings of the 4th IEEE International Symposium on Biomedical Imaging. Arlington, VA, 77-+. https://doi.org/10.1109/ICCV.2017.322Google ScholarCross Ref
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