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.

Related papers

• NeuroFly: A framework for whole-brain single neuron reconstruction [17.93211301158225]
  We introduce NeuroFly, a validated framework for large-scale automatic single neuron reconstruction. NeuroFly breaks down the process into three distinct stages: segmentation, connection, and proofreading. Our goal is to foster collaboration among researchers to address the neuron reconstruction challenge.
  arXiv  Detail & Related papers  (2024-11-07T13:56:13Z)
• Leveraging Frequency Domain Learning in 3D Vessel Segmentation [50.54833091336862]
  In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models. We show that our novel network achieves remarkable dice performance (84.37% on ASACA500 and 80.32% on ImageCAS) in tubular vessel segmentation tasks.
  arXiv  Detail & Related papers  (2024-01-11T19:07:58Z)
• Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing [72.45257414889478]
  We aim to reduce human workload by predicting connectivity between over-segmented neuron pieces. We first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain. We propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding.
  arXiv  Detail & Related papers  (2024-01-05T19:45:12Z)
• Geometric Learning-Based Transformer Network for Estimation of Segmentation Errors [1.376408511310322]
  We propose an approach to identify and measure erroneous regions in the segmentation map. Our method can estimate error at any point or node in a 3D mesh generated from a possibly erroneous volumetric segmentation map. We have evaluated our network on a high-resolution micro-CT dataset of the human inner-ear bony labyrinth structure.
  arXiv  Detail & Related papers  (2023-08-09T16:58:03Z)
• Benchmarking Graph Neural Networks for FMRI analysis [0.0]
  Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from graph-structured data. We study and evaluate the performance of five popular GNN architectures in diagnosing major depression disorder and autism spectrum disorder. We highlight that creating optimal graph structures for functional brain data is a major bottleneck hindering the performance of GNNs.
  arXiv  Detail & Related papers  (2022-11-16T14:16:54Z)
• PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Clouds [18.738943602529805]
  We propose a novel framework for 3D neuron reconstruction. Our key idea is to use the geometric representation power of the point cloud to better explore the intrinsic structural information of neurons.
  arXiv  Detail & Related papers  (2022-10-15T14:11:56Z)
• Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction [27.836007480393953]
  We propose a novel voxel-level cross-volume representation learning paradigm on the basis of an encoder-decoder segmentation model. Our method introduces no extra cost during inference. Evaluated on 42 3D neuron images from BigNeuron project, our proposed method is demonstrated to improve the learning ability of the original segmentation model.
  arXiv  Detail & Related papers  (2021-08-14T12:17:45Z)
• PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images [83.26057031236965]
  We propose a pixel-wise sparse graph reasoning (PSGR) module to enhance the modeling of long-range dependencies for COVID-19 infected region segmentation in CT images. The PSGR module avoids imprecise pixel-to-node projections and preserves the inherent information of each pixel for global reasoning. The solution has been evaluated against four widely-used segmentation models on three public datasets.
  arXiv  Detail & Related papers  (2021-08-09T04:58:23Z)
• Topological obstructions in neural networks learning [67.8848058842671]
  We study global properties of the loss gradient function flow. We use topological data analysis of the loss function and its Morse complex to relate local behavior along gradient trajectories with global properties of the loss surface.
  arXiv  Detail & Related papers  (2020-12-31T18:53:25Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition [126.51241919472356]
  We design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
  arXiv  Detail & Related papers  (2020-11-26T14:43:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.