The goal is to segment and identify automatically mitochondria instances in EM images. To solve such task pairs of EM images and their corresponding instance segmentation labels are provided. Below a pair example is depicted:

MitoEM dataset is composed by two EM volumes from human and rat cortices, named MitoEM-H and MitoEM-R respectively. Each volume has a size of (1000,4096,4096) voxels, for (z,x,y) axes. Both tissues contain multiple instances entangling with each other with unclear boundaries and complex morphology, e.g., (a) mitochondria-on-a-string (MOAS) instances are connected by thin microtubules, and (b) multiple instances can entangle with each other.

Citation:

Wei, Donglai, et al. "Mitoem dataset: Large-scale 3d mitochondria instance segmentation from em images." International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer International Publishing, 2020.