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MitoEM 2.0: A Benchmark for Challenging 3D Mitochondria Instance Segmentation from EM Images.

Peng Liu¹, Boyu Shen¹, Liyuan Liu¹

¹Boston College.

Biorxiv : the Preprint Server for Biology

|February 25, 2026

Summary

MitoEM 2.0 is a new dataset for training 3D mitochondria segmentation in electron microscopy. It features expert labels for complex scenarios, enabling robust algorithm development and benchmarking.

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Area of Science:

Cell Biology
Biophysics
Computer Vision

Background:

Accurate 3D mitochondria segmentation in volume electron microscopy (vEM) is crucial for understanding cellular function.
Existing datasets often lack the complexity and expert annotation required for robust model training and evaluation.

Purpose of the Study:

To introduce MitoEM 2.0, a comprehensive dataset for training and evaluating 3D mitochondria instance segmentation algorithms.
To provide standardized, expert-verified data covering challenging biological scenarios.

Main Methods:

Assembled multiscale vEM datasets (FIB-SEM, SBF-SEM, ssSEM) from diverse tissues and species.
Generated expert-verified instance labels focusing on dense packing, fused networks, and ambiguous boundaries.
Provided native and processed volumes, metadata, standardized splits, and NIfTI format.

Main Results:

MitoEM 2.0 includes challenging datasets with high-quality, expert annotations.
The dataset enables reproducible benchmarking of segmentation methods.
Baseline scripts and size-stratified evaluation are provided.

Conclusions:

MitoEM 2.0 facilitates robust model development for 3D mitochondria segmentation.
The resource supports fair comparison across different algorithms.
It serves as a valuable tool for bioimage analysis, algorithm benchmarking, and education.