Abstract
The pipeline of connectomics usually divides the large-scale electron microscopy volumes into multiple 3D blocks and segments them independently. The segmentation results in adjacent blocks demand subtle merging so that corresponding neurons can be correctly stitched. In this paper, we propose the first deep learning based neuron stitching method for connectomics. Specifically, we densely slide a 3D window along the shared face of two adjacent blocks to generate the training and testing input. A classifier based on a 3D convolutional neural network is utilized to identify whether two instance objects from adjacent blocks should be merged. The stitching label is obtained from the in-block segmentation of dedicated blocks. Experimental results on isotropic and anisotropic datasets demonstrate that our stitching method outperforms state-of-the-art methods.
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Acknowledgement
This work was supported in part by Key Area R&D Program of Guangdong Province with grant No. 2018B030338001, Anhui Provincial Natural Science Foundation under grant No. 1908085QF256, National Natural Science Foundation of China under grant No. 61901435, 62076230 and University Synergy Innovation Program of Anhui Province No. GXXT-2019-025.
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Liu, X. et al. (2021). Learning Neuron Stitching for Connectomics. 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_42
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