Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images
This work addresses the critical step of neuronal structure segmentation for accurate neuron reconstruction in neuroscience, representing an incremental improvement over existing CNN-based methods.
The paper tackles the problem of segmenting single neurons from 3D optical microscope images, which is challenging due to noise and gaps, by proposing an end-to-end network that integrates graph reasoning and a skeleton-based auxiliary loss, resulting in performance exceeding counterpart algorithms on the Janelia dataset.
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.