Abstract
The lack of fully-annotated data sets is one of the major limiting factors in the application of learning-based segmentation approaches for microscopy image data. Especially for 3D image data, generation of such annotations remains a challenge, increasing the demand for approaches making most out of existing annotations. We propose a probabilistic approach to increase image data diversity in small annotated data sets without further cost, to improve and evaluate segmentation approaches and ultimately contribute to an increased efficacy of available annotations. Different experiments show utilization for benchmarking, image data augmentation and test-time augmentation on the example of a deep learning-based 3D segmentation approach. Code is publicly available at https://doi.org/https://github.com/stegmaierj/ImageDiversification.
This work was funded by the German Research Foundation DFG with the grant STE2802/2-1 (DE).
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References
Eschweiler, D., Rethwisch, M., Jarchow, M., Koppers, S., Stegmaier, J.: 3D fluorescence microscopy data synthesis for segmentation and benchmarking. PLoS ONE 16(12), e0260509 (2021)
Eschweiler, D., Stegmaier, J.: Robust 3D cell segmentation: extending the view of cellpose. In: IEEE International Conference in Image Processing (2022)
Meijering, E.: A bird’s-eye view of deep learning in bioimage analysis. Comput. Struct. Biotechnol. J. 18, 2312 (2020)
Meyer, M.I., de la Rosa, E., Pedrosa de Barros, N., Paolella, R., Van Leemput, K., Sima, D.M.: A contrast augmentation approach to improve multi-scanner generalization in MRI. Front. Neurosci. 1048 (2021)
Moshkov, N., Mathe, B., Kertesz-Farkas, A., Hollandi, R., Horvath, P.: Test-time augmentation for deep learning-based cell segmentation on microscopy images. Sci. Rep. 10(1), 1–7 (2020)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)
Stringer, C., Wang, T., Michaelos, M., Pachitariu, M.: Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18(1), 100–106 (2021)
Willis, L., et al.: Cell size and growth regulation in the arabidopsis thaliana apical stem cell niche. Proc. Natl. Acad. Sci. 113(51), E8238–E8246 (2016)
Zhao, A., Balakrishnan, G., Durand, F., Guttag, J.V., Dalca, A.V.: Data augmentation using learned transformations for one-shot medical image segmentation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8543–8553 (2019)
Zhou, Z., Sodha, V., Pang, J., Gotway, M.B., Liang, J.: Models genesis. Med. Image Anal. 67, 101840 (2021)
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Eschweiler, D., Schock, J., Stegmaier, J. (2022). Probabilistic Image Diversification to Improve Segmentation in 3D Microscopy Image Data. In: Zhao, C., Svoboda, D., Wolterink, J.M., Escobar, M. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2022. Lecture Notes in Computer Science, vol 13570. Springer, Cham. https://doi.org/10.1007/978-3-031-16980-9_3
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DOI: https://doi.org/10.1007/978-3-031-16980-9_3
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