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Probabilistic Image Diversification to Improve Segmentation in 3D Microscopy Image Data

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Simulation and Synthesis in Medical Imaging (SASHIMI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13570))

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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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Correspondence to Dennis Eschweiler .

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16979-3

  • Online ISBN: 978-3-031-16980-9

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