Refining Mitochondria Segmentation in Electron Microscopy Imagery with Active Surfaces

Jorstad, Anne; Fua, Pascal

doi:10.1007/978-3-319-16220-1_26

Cited by 17 publications

(11 citation statements)

References 14 publications

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“…In recent years, various attempts have been made to quantify the important properties of mitochondria from EM data. For isotropic image stacks, Jorstad et al took advantage of the fact that mitochondria have thick dark membranes and proposed an active surface-based method to refine the boundary surfaces of mitochondria for the purpose of segmentation (Jorstad and Fua, 2014 ). Rigamonti et al improved on the KernelBoost classifier by iteratively considering the previous segmentation results and original images.…”

Section: Introductionmentioning

confidence: 99%

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

Xiao

Chen

et al. 2018

Front. Neuroanat.

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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 registration and histogram equalization as image pre-processing steps to maintain the consistency of the dataset; (2) proposing an effective approach for 3D mitochondria segmentation based on a volumetric, residual convolutional and deeply supervised network; and (3) employing a 3D connection method to obtain the relationship of mitochondria and displaying the 3D reconstruction results. To our knowledge, we are the first researchers to utilize a 3D fully residual convolutional network with a deeply supervised strategy to improve the accuracy of mitochondria segmentation. The experimental results on anisotropic and isotropic EM volumes demonstrate the effectiveness of our method, and the Jaccard index of our segmentation (91.8% in anisotropy, 90.0% in isotropy) and F1 score of detection (92.2% in anisotropy, 90.9% in isotropy) suggest that our approach achieved state-of-the-art results. Our fully automated pipeline contributes to the development of neuroscience by providing neurologists with a rapid approach for obtaining rich mitochondria statistics and helping them elucidate the mechanism and function of mitochondria.

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Section: Introductionmentioning

confidence: 99%

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

Xiao

Chen

et al. 2018

Front. Neuroanat.

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“…Since the snake algorithm in our system utilizes parabolic arcs based on the energy maps, a perfect segmentation is not expected. However, the accuracy can be significantly increased by refining techniques described in a study by Jorstad and Fua (Jorstad and Fua, 2015) or a modified live-wire algorithm proposed in our previous study (Mumcuoglu et al, 2012). …”

Section: Discussionmentioning

confidence: 99%

A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria

Tasel

Mumcuoglu

Hassanpour

et al. 2016

Journal of Structural Biology

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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 14 nm respectively.

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“…This energy function should be minimal when the contour is delineating the object of interest. Active contours have been applied extensively in a broad range of microscopy applications such as phase contrast [10], confocal [14] and EM [12] due to the possibility of designing very application-specific energy functions.…”

Section: Active Contour Segmentationmentioning

confidence: 99%

Decreasing Time Consumption of Microscopy Image Segmentation Through Parallel Processing on the GPU

Roels

Vylder

Saeys

et al. 2016

Advanced Concepts for Intelligent Vision Systems

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The computational performance of graphical processing units (GPUs) has improved significantly. Achieving speedup factors of more than 50x compared to single-threaded CPU execution are not uncommon due to parallel processing. This makes their use for high throughput microscopy image analysis very appealing. Unfortunately, GPU programming is not straightforward and requires a lot of programming skills and effort. Additionally, the attainable speedup factor is hard to predict, since it depends on the type of algorithm, input data and the way in which the algorithm is implemented. In this paper, we identify the characteristic algorithm and data-dependent properties that significantly relate to the achievable GPU speedup. We find that the overall GPU speedup depends on three major factors: 1) the coarse-grained parallelism of the algorithm, 2) the size of the data and 3) the computation/memory transfer ratio. This is illustrated on two types of well-known segmentation methods that are extensively used in microscopy image analysis: SLIC superpixels and high-level geometric active contours. In particular, we find that our used geometric active contour segmentation algorithm is very suitable for parallel processing, resulting in acceleration factors of 50x for 0.1 megapixel images and 100x for 10 megapixel images.

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Refining Mitochondria Segmentation in Electron Microscopy Imagery with Active Surfaces

Cited by 17 publications

References 14 publications

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

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

A validated active contour method driven by parabolic arc model for detection and segmentation of mitochondria

Decreasing Time Consumption of Microscopy Image Segmentation Through Parallel Processing on the GPU

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