Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network.

ABSTRACT: Recent studies have supported the relation between mitochondrial functions and degenerative disorders related to ageing, such as Alzheimer's and Parkinson's diseases. Since these studies have exposed the need for detailed and high-resolution analysis of physical alterations in mitochondria, it is necessary to be able to perform segmentation and 3D reconstruction of mitochondria. However, due to the variety of mitochondrial structures, automated mitochondria segmentation and reconstruction in electron microscopy (EM) images have proven to be a difficult and challenging task. This paper puts forward an effective and automated pipeline based on deep learning to realize mitochondria segmentation in different EM images. The proposed pipeline consists of three parts: (1) utilizing image registra

SUBMITTER: Xiao C 

PROVIDER: S-EPMC6224513 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature