Abstract
We introduce the idea of ‘image enrichment’ whereby the information content of images is increased in order to enhance segmentation accuracy. Unlike in data augmentation, the focus is not on increasing the number of training samples (by adding new virtual samples), but on increasing the information for each sample. For this purpose, we use a GAN-based image-to-image translation approach to generate corresponding virtual samples from a given (original) image. The virtual samples are then merged with the original sample to create a multi-channel image, which serves as the enriched image. We train and test a segmentation network on enriched images showing kidney pathology and obtain segmentation scores exhibiting an improvement compared to conventional processing of the original images only. We perform an extensive evaluation and discuss the reasons for the improvement.
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Notes
- 1.
We use the PyTorch reference implementation [9].
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Acknowledgment
This work was supported by the German Research Foundation (DFG) under grant no. ME3737/3-1.
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Gupta, L., Klinkhammer, B.M., Boor, P., Merhof, D., Gadermayr, M. (2019). GAN-Based Image Enrichment in Digital Pathology Boosts Segmentation Accuracy. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_70
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DOI: https://doi.org/10.1007/978-3-030-32239-7_70
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