Adaptive active-mask image segmentation for quantitative characterization of mitochondrial morphology

We propose an automated algorithm for segmentation of mitochondria from widefield fluorescence microscopy images for quantitative morphology characterization. Mitochondria are membrane-bound organelles that are essential to cells of higher living organisms. Reliable and precise quantitative characterization of their shape is crucial to understanding related physiology and disease mechanisms. Building upon the active-mask framework developed for segmentation of confocal fluorescence microscope images, we propose a new adaptive region-based distributing function to effectively address the problem of halo artifacts that are common in widefield fluorescence images. Such artifacts prevent the segmentation of weak features of mitochondria using existing algorithms. We compare the algorithm to the original active-mask algorithm as well as the geodesic active contour algorithm based on hand-segmented ground truth, and find that it performs significantly better both qualitatively and quantitatively.