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An effective fully deep convolutional neural networks for mitochondria segmentation based on ATUM-SEM
Author(s): Chi Xiao; Weifu Li; Xi Chen; Hua Han; Qiwei Xie
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Paper Abstract

Recent studies have empowered that the relation between mitochondrial function and degenerative disorder- s is related to aging diseases. Due to the rapid development of electron microscope (EM), stacks delivered by EM can be used to investigate the link between mitochondrial function and physical structure. Whereas, one of the main challenges in mitochondria research is developing suitable segmentation algorithms to obtain the shapes of mitochondria. Nowadays, Deep Neural Network (DNN) has been widely applied in solving the neuron membrane segmentation problems in virtue of its exceptional performance. For this reason, its appli- cation to mitochondria segmentation holds great promise. In this paper, we propose an effective deep learning approach to realize mitochondria segmentation in Automated Tape-Collecting Ultra Microtome Scanning Elec- tron Microscopy (ATUM-SEM) stacks. The proposed algorithm contains three parts: (1) utilizing histogram equalization algorithm as image preprocessing to keep the consistency of dataset; (2) putting forward a fusion fully convolution network (FCN), which is motivated by the principle the deeper, the better, to build a much deeper network architecture for more accurate mitochondria segmentation; and (3) employing fully connected conditional random field (CRF) to optimize segmentation results. Evaluation was performed on a dataset of a stack of 31 slices from ATUM-SEM, with 20 images used for training and 11 images for testing. For comparison, U-Net approach was evaluated through the same dataset. Jaccard index between the automated segmentation and expert manual segmentations indicates that our method (90.1%) outperforms U-Net (87.9%) and has a preferable performance on mitochondria segmentation with different shapes and sizes.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740C (2 March 2018); doi: 10.1117/12.2293291
Show Author Affiliations
Chi Xiao, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Weifu Li, Institute of Automation (China)
Hubei Univ. (China)
Xi Chen, Institute of Automation (China)
Hua Han, Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Hubei Univ. (China)
Qiwei Xie, Institute of Automation (China)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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