Abstract
Nuclei segmentation is an indispensable prerequisite for microscope image analyses. However, a successful instance segmentation result is still challenging attributable to the ubiquitous presence of clustered nuclei, as well as the morphological variation among dissimilar phenotype of cells. In this paper, a novel contour-aware 2.5-path decoder network (CA2.5-Net) is proposed for nuclei segmentation in microscope images. In contrast to the regular two-path decoders in many previous contour-aware networks, a shared decoder path is employed when the clustered-edge problem is severe. The range of recognizability difficulty generated by the extra half path also serves as a natural proxy to construct a curriculum-learning model, where training samples are sequenced for a better segmentation performance. Last, in this paper, we publicize two well-annotated privately-owned datasets covering a wide range of difficulty in the nuclei segmentation task, comprising 500 confocal microscopy image patches of deep-sea archaea and drosophila embryos obtained from 2013 to 2020. In the benchmark test of these two own datasets and one open-source set, our model outperforms the state-of-the-art nuclei segmentation approaches by a large margin, evaluated by the metrics of Average Jaccard Index and Dice score. Empirically, the proposed structure triples the training convergence speed in comparison with the competing CIA-net and BRP-net structures in nuclei segmentation.
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Huang, J., Shen, Y., Shen, D., Ke, J. (2021). CA2.5-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12908. Springer, Cham. https://doi.org/10.1007/978-3-030-87237-3_43
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DOI: https://doi.org/10.1007/978-3-030-87237-3_43
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