U-RISC: an ultra-high-resolution electron microscopy dataset challenging existing deep learning algorithms

Abstract

Connectomics is a developing filed aiming at reconstructing the connection of neural system at nanometer scale. Computer vision technology, especially deep learning methods used in image processing, has promoted connectomic data analysis to a new era. Even though, the performance of the state-of-the-art methods is still fall behind the demand of researchers. To alleviate this situation, here we first introduce a new annotated Ultra-high Resolution Image Segmentation dataset for the Cell membrane, called U-RISC, which is the largest annotated Electron Microscopy (EM) dataset for the cell membrane with multiple iterative annotations and the resolution of 2.18nm/pixel. Then we reveal the performance of existing deep learning segmentation methods on U-RISC through an open competition. The performance of participants appears to have a huge gap with human-level, however, the results of same methods on ISBI 2012, a smaller EM dataset, are near-human. To further explore the differences between the performance of two datasets, we analyze the neural networks with attribution analysis and uncover the larger decision-making area in the segmentation of U-RISC. Our work provides a new benchmark data for EM cell membrane segmentation and proposes some perspectives in deep learning segmentation algorithms.