Mitochondria, as tiny structures within the cell, are of significant importance to study cell
functions for biological and clinical analysis. And exploring how to automatically segment …

Electron microscopy (EM) enables high-resolution imaging of tissues and cells based on 2D
and 3D imaging techniques. Due to the laborious and time-consuming nature of manual …

Existing methods for unsupervised domain adaptive mitochondria segmentation perform
feature alignment via adversarial learning, and achieve promising performance. However …

Automatic mitochondrial segmentation enjoys great popularity with the development of deep
learning. However, the coarse prediction raised by the presence of regular 3D grids in …

Mitochondria instance segmentation from electron microscopy (EM) images has seen
notable progress since the introduction of deep learning methods. In this paper, we propose …

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance
segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI …

Deep learning-based methods for mitochondria segmentation require sufficient annotations
on Electron Microscopy (EM) volumes, which are often expensive and time-consuming to …

Abstract 3D mitochondria segmentation in electron microscopy (EM) images has achieved
significant progress. However, existing learning-based methods with high performance …

Recent advances in deep learning have greatly improved the segmentation of mitochondria
from Electron Microscopy (EM) images. However, suffering from variations in mitochondrial …

Accurate 3D mitochondria instance segmentation in electron microscopy (EM) is a
challenging problem and serves as a prerequisite to empirically analyze their distributions …