MitoNet automatic instance segmentation of mitochondria in the OpenOrganelle Mouse Kidney dataset

MitoNet is a generalist model for the instance segmentation of mitochondria in electron microscopy images. This dataset is the automatically generated result from applying MitoNet to the large OpenOrganelle Mouse Kidney FIB-SEM dataset at 16 nm voxel size. It contains nearly 220,000 segmented objects stored in a chunked Zarr array with chunk size of (512, 512, 512). <br> This version of MitoNet predicted both the semantic segmentation, to distinguish mitochondria from background, and contours of each instance. Predictions were hardened at a confidence threshold of 0.5 for the semantic segmentation and 0.3 for the contour segmentation. Connected components and the watershed algorithm were then applied to create an instance segmentation. Instances that were found to be smaller than 4,000 voxels were removed as likely false positives. <br> Inference took 4 hours on a single Biowulf compute node equipped with a A100 GPU, 64 GB RAM, and 32 CPUs cores. <br> The data file contains a Zarr directory that can be loaded in Python for further analysis or Napari for visualization.MitoNet是一款用于电子显微镜图像中线粒体实例分割的通用模型。本数据集是将MitoNet应用于体素尺寸为16 nm的大型OpenOrganelle小鼠肾脏FIB-SEM数据集后自动生成的结果，包含近22万个分割目标，存储于分块尺寸为(512, 512, 512)的分块Zarr数组中。 本版本的MitoNet同时预测了语义分割结果（用于区分线粒体与背景）与各实例的轮廓。针对语义分割任务，模型以0.5的置信度阈值对预测结果进行硬筛选；针对轮廓分割任务，则采用0.3的置信度阈值。随后通过连通分量算法与分水岭算法生成实例分割结果。对于体素数少于4000的实例，将其作为疑似假阳性样本移除。 在搭载A100图形处理器（GPU）、64 GB内存以及32个CPU核心的单个Biowulf计算节点上，模型推理耗时4小时。 该数据文件包含一个Zarr目录，可通过Python加载以开展后续分析，或通过Napari进行可视化。