Computerized detection and segmentation of mitochondria on electron microscope images

Machine learning-based 3D segmentation of mitochondria in polarized epithelial cells

Mitochondria are dynamic organelles that alter their morphological characteristics in response to functional needs. Therefore, mitochondrial morphology is an important indicator of mitochondrial function and cellular health. Reliable segmentation of mitochondrial networks in microscopy images is a crucial initial step for further quantitative evaluation of their morphology. However, 3D mitochondrial segmentation, especially in cells with complex network morphology, such as in highly polarized cells, remains challenging. To improve the quality of 3D segmentation of mitochondria in super-resolution microscopy images, we took a machine learning approach, using 3D Trainable Weka, an ImageJ plugin. We demonstrated that, compared with other commonly used methods, our approach segmented mitochondrial networks effectively, with improved accuracy in different polarized epithelial cell models, including differentiated human retinal pigment epithelial (RPE) cells. Furthermore, using several tools for quantitative analysis following segmentation, we revealed mitochondrial fragmentation in bafilomycin-treated RPE cells.

Evaluation of Mitochondria Content and Function in Live Cells by Multicolor Flow Cytometric Analysis

To evaluate how a cell responds to the external stimuli, treatment, or alteration of the microenvironment, the quantity and quality of mitochondria are commonly used as readouts. However, it is challenging to apply mitochondrial analysis to the samples that are composed of mixed cell populations originating from tissues or when multiple cell populations are of interest, using methods such as Western blot, electron microscopy, or extracellular flux analysis.Flow cytometry is a technique allowing the detection of individual cell status and its identity simultaneously when used in combination with surface markers. Here we describe how to combine mitochondria-specific dyes or the dyes targeting the superoxide produced by mitochondria with surface marker staining to measure the mitochondrial content and activity in live cells by flow cytometry. This method can be applied to all types of cells in suspension and is particularly useful for analysis of samples composed of heterogeneous cell populations.

A Novel 3D Connection Algorithm of Mitochondria From ATUM-SEM Stacks Based on Segmentation Information in Context

Recent researches have shown that the relation between mitochondrial function and degenerative disorders is closely related to aging, such as Alzheimer's and Parkinson's diseases. Because these studies expose the need for detailed analysis of high-resolution physical alterations in mitochondria, three dimensional (3D) visualization of mitochondria from electron microscopy (EM) images is coming into prominence. To this end, how to develop suitable segmentation algorithms and connection algorithms has attracted our attentions. Since previous algorithms have shown preferable segmentation performance on mitochondria with different shapes and sizes. In this paper, we propose to utilize the segmentation information instead of detection information in context to obtain the mitochondrial connection relation in adjacent layers. Additionally, different from previous methods, we present a novel and effective connection approach by obtaining sparse matrixes and implementing a forward connection mode. Experiments on automated tape-collecting ultramicrotome scanning electron microscopy (ATUM-SEM) stacks demonstrate that our approach can effectively handle with the case of split and merge, and achieve a comparable connection quality measured by split error and merge error.

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

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.

An automated pipeline for mitochondrial segmentation on ATUM-SEM stacks

It is possible now to look more closely into mitochondrial physical structures due to the rapid development of electron microscope (EM). Mitochondrial physical structures play important roles in both cellular physiology and neuronal functions. Unfortunately, the segmentation of mitochondria from EM images has proven to be a difficult and challenging task, due to the presence of various subcellular structures, as well as image distortions in the sophisticated background. Although the current state-of-the-art algorithms have achieved some promising results, they have demonstrated poor performances on these mitochondria which are in close proximity to vesicles or various membranes. In order to overcome these limitations, this study proposes explicitly modelling the mitochondrial double membrane structures, and acquiring the image edges by way of ridge detection rather than by image gradient. In addition, this study also utilizes group-similarity in context to further optimize the local misleading segmentation. Then, the experimental results determined from the images acquired by automated tape-collecting ultramicrotome scanning electron microscopy (ATUM-SEM) demonstrate the effectiveness of this study's proposed algorithm.

Multiplexed high-content analysis of mitochondrial morphofunction using live-cell microscopy

Mitochondria have a central role in cellular (patho)physiology, and they display a highly variable morphology that is probably coupled to their functional state. Here we present a protocol that allows unbiased and automated quantification of mitochondrial 'morphofunction' (i.e., morphology and membrane potential), cellular parameters (size, confluence) and nuclear parameters (number, morphology) in intact living primary human skin fibroblasts (PHSFs). Cells are cultured in 96-well plates and stained with tetramethyl rhodamine methyl ester (TMRM), calcein-AM (acetoxy-methyl ester) and Hoechst 33258. Next, multispectral fluorescence images are acquired using automated microscopy and processed to extract 44 descriptors. Subsequently, the descriptor data are subjected to a quality control (QC) algorithm based upon principal component analysis (PCA) and interpreted using univariate, bivariate and multivariate analysis. The protocol requires a time investment of ∼4 h distributed over 2 d. Although it is specifically developed for PHSFs, which are widely used in preclinical research, the protocol is portable to other cell types and can be scaled up for implementation in high-content screening.

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

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.

Resolving presynaptic structure by electron tomography

A key goal in neurobiology is to generate a theoretical framework that merges structural, physiological, and molecular explanations of brain function. These categories of explanation do not advance in synchrony; advances in one category define new experiments in other categories. For example, the synapse was defined physiologically and biochemically before it was visualized using electron microscopy. Indeed, the original descriptions of synapses in the 1950s were lent credence by the presence of spherical vesicles in presynaptic terminals that were considered to be the substrate for quantal neurotransmission. In the last few decades, our understanding of synaptic function has again been driven by physiological and molecular techniques. The key molecular players for synaptic vesicle structure, mobility and fusion were identified and applications of the patch clamp technique permitted physiological estimation of neurotransmitter release and receptor properties. These advances demand higher resolution structural images of synapses. During the 1990s a second renaissance in cell biology driven by EM was fueled by improved techniques for electron tomography (ET) with the ability to compute virtual images with nm resolution between image planes. Over the last 15 years, ET has been applied to the presynaptic terminal with special attention to the active zone and organelles of the nerve terminal. In this review, we first summarize the technical improvements that have led to a resurgence in utilization of ET and then we summarize new insights gained by the application of ET to reveal the high-resolution structure of the nerve terminal.

Automatic segmentation of mitochondria in EM data using pairwise affinity factorization and graph-based contour searching

In this paper, we investigate the segmentation of closed contours in subcellular data using a framework that primarily combines the pairwise affinity grouping principles with a graph partitioning contour searching approach. One salient problem that precluded the application of these methods to large scale segmentation problems is the onerous computational complexity required to generate comprehensive representations that include all pairwise relationships between all pixels in the input data. To compensate for this problem, a practical solution is to reduce the complexity of the input data by applying an over-segmentation technique prior to the application of the computationally demanding strands of the segmentation process. This approach opens the opportunity to build specific shape and intensity models that can be successfully employed to extract the salient structures in the input image which are further processed to identify the cycles in an undirected graph. The proposed framework has been applied to the segmentation of mitochondria membranes in electron microscopy data which are characterized by low contrast and low signal-to-noise ratio. The algorithm has been quantitatively evaluated using two datasets where the segmentation results have been compared with the corresponding manual annotations. The performance of the proposed algorithm has been measured using standard metrics, such as precision and recall, and the experimental results indicate a high level of segmentation accuracy.

Local regularization of tilt projections reduces artifacts in electron tomography

Electron tomography produces very high resolution 3D image volumes useful for investigating the structure and function of cellular components. Unfortunately, unavoidable discontinuities and physical constraints in the acquisition geometry lead to a range of artifacts that can affect the reconstructed image. In particular, highly electron dense regions, such as gold nanoparticles, can hide proximal biological structures and degrade the overall quality of the reconstructed tomograms. In this work we introduce a pre-reconstruction non-conservative non-linear isotropic diffusion (NID) filter that automatically identifies and reduces local irregularities in the tilt projections. We illustrate the improvement in quality obtained using this approach for reconstructed tomograms generated from samples of malaria parasite-infected red blood cells. A quantitative and qualitative evaluation for our approach on both simulated and real data is provided.