ArticleJournal of structural biology2016
A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria.
Article in Journal of structural biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 22 citations in OpenAlex.
- Tooth segmentation and dental crowding diagnosis using two-stage dual-dilated graph convolution.International journal of computer assisted radiology and surgery · 2026Article
- Automatic Mitochondria Segmentation for EM Data Using a 3D Supervised Convolutional Network.Frontiers in neuroanatomy · 2018Article
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Recent studies reveal that mitochondria take substantial responsibility in cellular functions that are closely related to aging diseases caused by degeneration of neurons. These studies emphasize that the membrane and crista morphology of a mitochondrion should receive attention in order to investigate the link between mitochondrial function and its physical structure. Electron microscope tomography (EMT) allows analysis of the inner structures of mitochondria by providing highly detailed visual data from large volumes. Computerized segmentation of mitochondria with minimum manual effort is essential to accelerate the study of mitochondrial structure/function relationships. In this work, we improved and extended our previous attempts to detect and segment mitochondria from transmission electron microcopy (TEM) images. A parabolic arc model was utilized to extract membrane structures. Then, curve energy based active contours were employed to obtain roughly outlined candidate mitochondrial regions. Finally, a validation process was applied to obtain the final segmentation data. 3D extension of the algorithm is also presented in this paper. Our method achieved an average F-score performance of 0.84. Average Dice Similarity Coefficient and boundary error were measured as 0.87 and 14nm respectively.
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Registered trials
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