MitoEM dataset: large-scale 3d mitochondria instance segmentation
This tutorial describes how to reproduce the results reported in our paper, concretely
U2D-BC to make instance segmentation of mitochondria in electron microscopy (EM) images:
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.
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. They are divided in
(100,4096,4096) for validation and
(500,4096,4096) for test. 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.
You need to download MitoEM dataset first:
The EM images should be
1000 on each case while labels only
500 are available. The partition should be as follows:
Training data is composed by
400images, i.e. EM images and labels in
[0-399]range. These labels are separated in a folder called
Validation data is composed by
100images, i.e. EM images and labels in
[400-499]range. These labels are separated in a folder called
Test data is composed by
500images, i.e. EM images in
Once you have donwloaded this data you need to create a directory tree as described in Data preparation.
To reproduce the exact results of our manuscript you need to use mitoem.yaml configuration file.
Then you need to modify
TRAIN.GT_PATH with your training data path of EM images and labels respectively. In the same way, do it for the validation data with
VAL.GT_PATH and for the test setting
To run it via command line or Docker you can follow the same steps as decribed in Run.
The results follow same structure as explained in Results. The results should be something like the following:
MitoEM challenge submission
There is a open challenge for MitoEM dataset: https://mitoem.grand-challenge.org/
.h5 files from resulting instance predictions in
.tif format you can use the script tif_to_h5.py. The instances of both Human and Rat tissue need to be provided
(files must be named as
1_rat_instance_seg_pred.h5 respectively). Find the full
details in the challenge’s evaluation page.