Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images

• URL: http://arxiv.org/abs/2101.08910v1
• Date: Fri, 22 Jan 2021 01:27:14 GMT
• Title: Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images
• Authors: Heng Wang, Yang Song, Chaoyi Zhang, Jianhui Yu, Siqi Liu, Hanchuan Peng, Weidong Cai
• Abstract summary: We present an end-to-end segmentation network by jointly considering the local appearance and the global geometry traits. The evaluation results on the Janelia dataset from the BigNeuron project demonstrate that our proposed method exceeds the counterpart algorithms in performance.
• Score: 30.539098538610013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the critical steps in improving accurate single neuron reconstruction from three-dimensional (3D) optical microscope images is the neuronal structure segmentation. However, they are always hard to segment due to the lack in quality. Despite a series of attempts to apply convolutional neural networks (CNNs) on this task, noise and disconnected gaps are still challenging to alleviate with the neglect of the non-local features of graph-like tubular neural structures. Hence, we present an end-to-end segmentation network by jointly considering the local appearance and the global geometry traits through graph reasoning and a skeleton-based auxiliary loss. The evaluation results on the Janelia dataset from the BigNeuron project demonstrate that our proposed method exceeds the counterpart algorithms in performance.

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