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.

中文翻译：

---

mEMbrain：一种交互式深度学习 MATLAB 工具，用于在商用桌面上进行连接组分割

连接组学对于推动我们理解神经系统的组织、挖掘细胞以及从体积电子显微镜 (EM) 数据集重建的接线图至关重要。一方面，此类重建受益于更加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，已经表明需要用户友好的开源工具，使社区能够进行高级分析。根据第二条脉络，我们在这里提出了 mEMbrain，这是一种基于 MATLAB 的交互式软件，它包含算法和函数，可以在与 Linux 和 Windows 兼容的用户友好的用户界面中对电子显微镜数据集进行标记和分割。通过作为 API 集成到体注释和分割工具 VAST，mEMbrain 涵盖了地面实况生成、图像预处理、深度神经网络训练以及用于校对和评估的动态预测等功能。我们工具的最终目标是加快手动标记工作，并通过一系列半自动方法来利用 MATLAB 用户进行实例分割。我们在各种数据集上测试了我们的工具，这些数据集涵盖不同物种、不同尺度、神经系统区域和发育阶段。为了进一步加快连接组学的研究，我们提供了来自四种不同动物和五个数据集的地面实况注释的 EM 资源，总计约 180 小时的专家注释，产生超过 1.2 GB 的带注释的 EM 图像。此外，我们还为所述数据集提供了一组四个预训练网络。所有工具均可从https://lichtman.rc.fas.harvard.edu/mEMbrain/。通过我们的软件，我们希望为基于实验室的神经重建提供一种解决方案，不需要用户编码，从而为经济实惠的连接组学铺平道路。