Deep learning approaches in electron microscopy imaging for mitochondria segmentation. (2018)

• Record Type: Journal Article
• Authors: Oztel, Ismail; Yolcu, Gozde; Ersoy, Ilker; White, Tommi A.; Bunyak, Filiz
• Is Part Of: International journal of data mining and bioinformatics. Volume 21:Number 2(2018)
• Page Start: 91
• Page End: 106
• Publication Date: 2018
• Subjects: deep learning -- convolutional neural networks -- image segmentation -- electron microscopy -- mitochondria; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Deep neural networks provide outstanding classification and detection accuracy in biomedical imaging applications. We present a study for mitochondria segmentation in electron microscopy (EM) images. Mitochondria play a significant role in cell cycle by generating the needed energy, and show quantifiable morphological differences with diseases such as cancer, metabolic disorders, and neurodegeneration. EM imaging allows researchers to observe the morphological changes in cells as part of disease process at a high resolution. Manual segmentation of mitochondria in large sequences of EM images is time consuming and prone to subjective delineation. Thus, manual segmentation may not provide the high accuracy needed for accurate quantification of morphological changes. We show that a convolutional neural network provides accurate mitochondria segmentation in CA1 hippocampus area of brain that is imaged by a focused ion beam scanning electron microscope (FIBSEM). We compare our results with other studies which report results on the same data set and with other deep neural network approaches, and provide quantitative comparison.