Abstract
Deep learning algorithms for image segmentation typically require large data sets with high-quality annotations to be trained with. For many domains, the annotation cost for obtaining such sets may prove to be prohibitively expensive. Our work aims to reduce the time necessary to create high-quality annotated images by using a relatively small well-annotated data set for training a convolutional neural network to upgrade lower-quality annotations, produced at lower annotation costs. We apply our method to the task of cell segmentation and investigate the performance of our solution when upgrading annotation quality for labels affected by three types of annotation errors: omission, inclusion, and bias. We observe that our method is able to upgrade annotations affected by high error levels from 0.3 to 0.9 Dice similarity with the ground-truth annotations. Moreover, we show that a relatively small well-annotated set enlarged with samples with upgraded annotations can be used to train better-performing segmentation networks compared to training only on the well-annotated set.
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Acknowledgment
This research was supported by the SAILS program of Leiden University. DMP is supported by The Netherlands Organisation for Scientific Research (NWO), project number 016.Veni.192.235.
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Vădineanu, Ş., Pelt, D.M., Dzyubachyk, O., Batenburg, K.J. (2023). Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations. In: Xue, Z., et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_1
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