Method: automatic segmentation of mitochondria utilizing patch classification, contour pair classification, and automatically seeded level sets

<p>Abstract</p> <p>Background</p> <p>While progress has been made to develop automatic segmentation techniques for mitochondria, there remains a need for more accurate and robust techniques to delineate mitochondria in serial blockface scanning electron microscopic data. Previously developed texture based methods are limited for solving this problem because texture alone is often not sufficient to identify mitochondria. This paper presents a new three-step method, the Cytoseg process, for automated segmentation of mitochondria contained in 3D electron microscopic volumes generated through serial block face scanning electron microscopic imaging. The method consists of three steps. The first is a random forest patch classification step operating directly on 2D image patches. The second step consists of contour-pair classification. At the final step, we introduce a method to automatically seed a level set operation with output from previous steps.</p> <p>Results</p> <p>We report accuracy of the Cytoseg process on three types of tissue and compare it to a previous method based on Radon-Like Features. At step 1, we show that the patch classifier identifies mitochondria texture but creates many false positive pixels. At step 2, our contour processing step produces contours and then filters them with a second classification step, helping to improve overall accuracy. We show that our final level set operation, which is automatically seeded with output from previous steps, helps to smooth the results. Overall, our results show that use of contour pair classification and level set operations improve segmentation accuracy beyond patch classification alone. We show that the Cytoseg process performs well compared to another modern technique based on Radon-Like Features.</p> <p>Conclusions</p> <p>We demonstrated that texture based methods for mitochondria segmentation can be enhanced with multiple steps that form an image processing pipeline. While we used a random-forest based patch classifier to recognize texture, it would be possible to replace this with other texture identifiers, and we plan to explore this in future work.</p

The pro-hormone secretogranin II regulates dense-core secretory granule biogenesis in catecholaminergic cells

Processes underlying the formation of dense core secretory granules (DCGs) of neuroendocrine cells are poorly understood. Here, we present evidence that DCG biogenesis is dependent on the secretory protein secretogranin (Sg) II, a member of the granin family of pro-hormone cargo of DCGs in neuroendocrine cells. Depletion of SgII expression in PC12 cells leads to a decrease in both the number and size of DCGs and impairs DCG trafficking of other regulated hormones. Expression of SgII fusion proteins in a secretory-deficient PC12 variant rescues a regulated secretory pathway. SgII-containing dense core vesicles share morphological and physical properties with bona fide DCGs, are competent for regulated exocytosis, and maintain an acidic luminal pH through the V-type H+-translocating ATPase. The granulogenic activity of SgII requires a pH gradient along this secretory pathway. We conclude that SgII is a critical factor for the regulation of DCG biogenesis in neuroendocrine cells, mediating the formation of functional DCGs via its pH-dependent aggregation at the trans-Golgi network