Deep Learning Segmentation of Chromogenic Dye RNAscope From Breast Cancer Tissue
- Author(s)
- Davidson, A; Morley-Bunker, A; Wiggins, G; Walker, L; Harris, G; Mukundan, R; kConFab Investigators;
- Journal Title
- Journal of Imaging Informatics in Medicine
- Publication Type
- Online publication before print
- Abstract
- RNAscope staining of breast cancer tissue allows pathologists to deduce genetic characteristics of the cancer by inspection at the microscopic level, which can lead to better diagnosis and treatment. Chromogenic RNAscope staining is easy to fit into existing pathology workflows, but manually analyzing the resulting tissue samples is time consuming. There is also a lack of peer-reviewed, performant solutions for automated analysis of chromogenic RNAscope staining. This paper covers the development and optimization of a novel deep learning method focused on accurate segmentation of RNAscope dots (which signify gene expression) from breast cancer tissue. The deep learning network is convolutional and uses ConvNeXt as its backbone. The upscaling portions of the network use custom, heavily regularized blocks to prevent overfitting and early convergence on suboptimal solutions. The resulting network is modest in size for a segmentation network and able to function well with little training data. This deep learning network was also able to outperform manual expert annotation at finding the positions of RNAscope dots, having a final F1 -score of 0.745. In comparison, the expert inter-rater F1 -score was 0.596.
- Keywords
- Cancer; Computational pathology; Deep learning; Image segmentation; Machine learning; RNAscope
- Department(s)
- Laboratory Research
- Publisher's Version
- https://doi.org/10.1007/s10278-024-01301-9
- Open Access at Publisher's Site
https://doi.org/10.1007/s10278-024-01301-9- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2024-11-28 05:45:45
Last Modified: 2024-11-28 06:29:14