DP2: Distributed 3D image segmentation using micro-labor workforce.

SUMMARY: This application note describes a new scalable semi-automatic approach, the Dual Point Decision Process, for segmentation of 3D structures contained in 3D microscopy. The segmentation problem is distributed to many individual workers such that each receives only simple questions regarding whether two points in an image are placed on the same object. A large pool of micro-labor workers available through Amazon's Mechanical Turk system provides the labor in a scalable manner. AVAILABILITY AND IMPLEMENTATION: Python-based code for non-commercial use and test data are available in the source archive at https://sites.google.com/site/imagecrowdseg/. CONTACT: rgiuly@ucsd.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Semi-automated neuron boundary detection and nonbranching process segmentation in electron microscopy images.

Neuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor-intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. This paper presents a method for neuron boundary detection and nonbranching process segmentation in electron microscopy images and visualizing them in three dimensions. It combines both automated segmentation techniques with a graphical user interface for correction of mistakes in the automated process. The automated process first uses machine learning and image processing techniques to identify neuron membranes that deliniate the cells in each two-dimensional section. To segment nonbranching processes, the cell regions in each two-dimensional section are connected in 3D using correlation of regions between sections. The combination of this method with a graphical user interface specially designed for this purpose, enables users to quickly segment cellular processes in large volumes.

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

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

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.

A lithographically-patterned, elastic multi-electrode array for surface stimulation of the spinal cord.

A new, scalable process for microfabrication of a silicone-based, elastic multi-electrode array (MEA) is presented. The device is constructed by spinning poly(dimethylsiloxane) (PDMS) silicone elastomer onto a glass slide, depositing and patterning gold to construct wires and electrodes, spinning on a second PDMS layer, and then micropatterning the second PDMS layer to expose electrode contacts. The micropatterning of PDMS involves a custom reactive ion etch (RIE) process that preserves the underlying gold thin film. Once completed, the device can be removed from the glass slide for conformal interfacing with neural tissue. Prototype MEAs feature electrodes smaller than those known to be reported on silicone substrate (60 microm diameter exposed electrode area) and were capable of selectively stimulating the surface of the in vitro isolated spinal cord of the juvenile rat. Stretchable serpentine traces were also incorporated into the functional PDMS-based MEA, and their implementation and testing is described.