Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States.
Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada.
Front Neural Circuits. 2023 Jun 15;17:952921. doi: 10.3389/fncir.2023.952921. eCollection 2023.
Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.
连接组学在推动我们对神经系统组织的理解方面具有基础性作用，它揭示了从体积电子显微镜 (EM) 数据集重建的细胞和布线图。此类重建一方面受益于越来越精确的自动分割方法，这些方法利用了复杂的深度学习架构和先进的机器学习算法；另一方面，整个神经科学领域，特别是图像处理领域，都需要用户友好且开源的工具，使社区能够进行高级分析。顺应这一趋势，我们在这里提出了 mEMbrain，这是一款基于 MATLAB 的交互式软件，它封装了可在与 Linux 和 Windows 兼容的用户友好界面中对电子显微镜数据集进行标记和分割的算法和功能。通过作为体积注释和分割工具 VAST 的 API 进行集成，mEMbrain 包含了用于生成真实标签、图像预处理、训练深度神经网络以及实时预测以进行校对和评估的功能。我们工具的最终目标是加快手动标记工作的速度，并为 MATLAB 用户提供一系列半自动方法，例如实例分割。我们在各种数据集上测试了我们的工具，这些数据集涵盖了不同物种、不同尺度、神经系统不同区域和不同发育阶段。为了进一步加速连接组学的研究，我们提供了来自四个不同动物和五个数据集的 EM 真实标签资源，共计约 180 小时的专家注释，生成了超过 12GB 的标注 EM 图像。此外，我们还提供了四个针对上述数据集预先训练的网络。所有工具都可以从 https://lichtman.rc.fas.harvard.edu/mEMbrain/ 获得。我们希望通过我们的软件为基于实验室的神经重建提供一个不需要用户编码的解决方案，从而为负担得起的连接组学铺平道路。
