AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions-猫眼课题宝

DOI：

10.1007/978-3-030-87193-2_17

发表时间：

2021-07

期刊：

ArXiv

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中科院分区：

其他

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作者：

D. Wei;Kisuk Lee;Hanyu Li;R. Lu;J. A. Bae;Zequan Liu;Lifu Zhang;M'arcia dos Santos;Zudi Lin;T. Uram;Xueying Wang;Ignacio Arganda-Carreras;Brian Matejek;N. Kasthuri;J. Lichtman;H. Pfister

电子显微镜（EM）能够在单个突触水平上重建神经回路，这对科学发现具有变革性意义。然而，由于复杂的形态，皮质轴突的准确重建已成为一个重大的挑战。更糟糕的是，没有公开可用的来自皮层的大规模EM数据集为轴突提供密集的地面真值分割，这使得难以开发和评估大规模轴突重建方法。为了解决这个问题，我们引入了AxonEM数据集，它由分别来自人类和小鼠皮层的两个EM图像体积组成。我们彻底校对了超过18，000个轴突实例，以提供密集的3D轴突实例分割，从而能够对轴突重建方法进行大规模评估。此外，我们为每个数据量密集地注释了9个用于训练的地面真值子量。有了这个，我们再现了两个已发表的国家的最先进的方法，并提供他们的评估结果作为基线。我们公开发布代码和数据， https://connectomics-bazaar.github.io/proj/AxonEM/index.html 促进先进方法的发展。

Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries. However, due to the complex morphology, an accurate reconstruction of cortical axons has become a major challenge. Worse still, there is no publicly available large-scale EM dataset from the cortex that provides dense ground truth segmentation for axons, making it difficult to develop and evaluate large-scale axon reconstruction methods. To address this, we introduce theAxonEMdataset, which consists of twomEM image volumes from the human and mouse cortex, respectively. We thoroughly proofread over 18,000 axon instances to provide dense 3D axon instance segmentation, enabling large-scale evaluation of axon reconstruction methods. In addition, we densely annotate nine ground truth subvolumes for training, per each data volume. With this, we reproduce two published state-of-the-art methods and provide their evaluation results as a baseline. We publicly release our code and data at https://connectomics-bazaar.github.io/proj/AxonEM/index.html to foster the development of advanced methods.

AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions

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