Computerized detection and segmentation of mitochondria on electron microscope images

Abstract

SummaryMitochondrial function plays an important role in the regulation of cellular life and death, including disease states. Disturbance in mitochondrial function and distribution can be accompanied by significant morphological alterations. Electron microscopy tomography (EMT) is a powerful technique to study the 3D structure of mitochondria, but the automatic detection and segmentation of mitochondria in EMT volumes has been challenging due to the presence of subcellular structures and imaging artifacts. Therefore, the interpretation, measurement and analysis of mitochondrial distribution and features have been time consuming, and development of specialized software tools is very important for high-throughput analyses needed to expedite the myriad studies on cellular events. Typically, mitochondrial EMT volumes are segmented manually using special software tools. Automatic contour extraction on large images with multiple mitochondria and many other subcellular structures is still an unaddressed problem. The purpose of this work is to develop computer algorithms to detect and segment both fully and partially seen mitochondria on electron microscopy images. The detection method relies on mitochondria's approximately elliptical shape and double membrane boundary. Initial detection results are first refined using active contours. Then, our seed point selection method automatically selects reliable seed points along the contour, and segmentation is finalized by automatically incorporating a live-wire graph search algorithm between these seed points. In our evaluations on four images containing multiple mitochondria, 52 ellipses are detected among which 42

Regulation and Quantification of Cellular Mitochondrial Morphology and Content

“…New developments in the EM field, including tomography and image processing tools, have extended the analytic potential by enabling 3D-reconstruction, detection and segmentation of mitochondria, which has proven to be powerful methods to study mitochondrial internal and external structure. Additional information regarding EM analysis of mitochondrial (ultra)structure can be found elsewhere [170][171][172][173][174][175][176][177][178][179][180]. Below we discuss three other methodological approaches to assess mitochondrial morphology and/or content in biological samples: (i) bioenergetic capacity, (ii) biochemical biomarkers, and (iii) fluorescence microscopy analysis.…”

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

“…In our preceding studies (Mumcuoglu et al, 2012; Tasel et al, 2014), a region was accepted as a correct detection if at least 70% of area of the region overlapped a single mitochondrion. Then, precision and recall were defined based on the number of regions (not the size of regions).…”

“…Considering that the typical membrane thickness of mitochondria is in the range of 4–6 nm (Róg et al, 2009), interpolating the image to 2 nm pixel size does not generate data loss problems for the mitochondrial membrane as we justified in our pilot study (Mumcuoglu et al, 2012). …”

“…In our previous attempt (Mumcuoglu et al, 2012), we employed a double membrane detection based on kernel pairs and an ellipse fitting approach for 2D detection and separation of mitochondria followed by active contours and a modified livewire method for 2D accurate segmentation of mitochondria. This algorithm depends on the successful removal of cristae in one of the intermediate steps to locate the peripheral mitochondrial membranes.…”

Snakes on a Plane: A perfect snap for bioimage analysis

“…Meanwhile, if a few but large objects need to be segmented where small details are important, an interactive scheme might be more suitable (see Figure 6). A vast literature exists on the use of snakes to segment biological structures such as biological tissues (nerve fibers [50], [51]), cell structures (mitochondria [52]), protein-based structures (actin filaments [53]), or model organisms such as zebra fish embryos [54] or C. elegans (see Figure 5). The versatile nature of snakes makes them suitable for problems that combine segmentation and tracking (Leukocyte tracking [55], motility analysis [56]), organelle tracking (microtubule tracking), or even the reconstruction of cell lineages [57].…”