Boundary Segmentation For Fluorescence Microscopy Using Steerable Filters

Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation

Bucillamine prevents cisplatin-induced ototoxicity through induction of glutathione and antioxidant genes.

Bucillamine is used for the treatment of rheumatoid arthritis. This study investigated the protective effects of bucillamine against cisplatin-induced damage in auditory cells, the organ of Corti from postnatal rats (P2) and adult Balb/C mice. Cisplatin increases the catalytic activity of caspase-3 and caspase-8 proteases and the production of free radicals, which were significantly suppressed by pretreatment with bucillamine. Bucillamine induces the intranuclear translocation of Nrf2 and thereby increases the expression of γ-glutamylcysteine synthetase (γ-GCS) and glutathione synthetase (GSS), which further induces intracellular antioxidant glutathione (GSH), heme oxygenase 1 (HO-1) and superoxide dismutase 2 (SOD2). However, knockdown studies of HO-1 and SOD2 suggest that the protective effect of bucillamine against cisplatin is independent of the enzymatic activity of HO-1 and SOD. Furthermore, pretreatment with bucillamine protects sensory hair cells on organ of Corti explants from cisplatin-induced cytotoxicity concomitantly with inhibition of caspase-3 activation. The auditory-brainstem-evoked response of cisplatin-injected mice shows marked increases in hearing threshold shifts, which was markedly suppressed by pretreatment with bucillamine in vivo. Taken together, bucillamine protects sensory hair cells from cisplatin through a scavenging effect on itself, as well as the induction of intracellular GSH

Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks

Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D

Report of the DAMENames Ad Hoc Committee

In early 2018, the DAMEid group requested that Cataloging and Metadata unit examine the metadata needs for the DAME. When analyzing metadata needs in both OAKTrust and Fedora, it became clear that the lack of name authority control was causing serious problems for users, especially in the case of a single author having many entries in the author index. For example, Steven M. Wright, Royce E. Wisenbaker, Professor II in Chemical and Electrical Engineering, has 10 different entries for his name. This problem is caused by the lack of authority control and the inconsistent ways in which names are inputted into Vireo and OAKTrust. In their report to the DAMEid committee, the Metadata and Cataloging librarians strongly suggested that some type of name authority control be implemented within the DAME. In smaller repositories with few names and fewer entities (e.g., persons, organizations, subjects, etc.), the absence of explicit disambiguation or authority control can be a manageable problem. When only a few authors share a name, it is easy to tell them apart based on the subject matter of the works attached to the name. The problem compounds as collections grow larger and the number of entities with the same name that need to be distinguished from each other increases. For example, in the large OAKTrust IR, it is hard for a user to identify the "Steven Wright" that he or she is looking for, as there are several authors so named with dozens of items in the IR. Another issue that emerges in a system with no authority control – such as OAKTrust – is that an everyday typographical error (an extra space, no period after an initial, misspellings, etc.) results in a new entry in the author list. This results in multiple names for one person and it means that there is no way for a user to easily identify all the works attributed to one author

DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data

The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seldom effective and/or robust. Techniques based upon deep-learning have shown great promise, but enthusiasm for applying these techniques is tempered by the need to generate training data, an arduous task, particularly in three dimensions. Here we present results of a new technique of nuclear segmentation using neural networks trained on synthetic data. Comparisons with results obtained using commonly-used image processing packages demonstrate that DeepSynth provides the superior results associated with deep-learning techniques without the need for manual annotation

Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets

mTOR Complex 2 Is Required for the Development of Prostate Cancer Induced by Pten Loss in Mice

mTOR complex 2 (mTORC2) contains the mammalian target of rapamycin (mTOR) kinase and the Rictor regulatory protein and phosphorylates Akt. Whether this function of mTORC2 is critical for cancer progression is unknown. Here, we show that transformed human prostate epithelial cells lacking PTEN require mTORC2 to form tumors when injected into nude mice. Furthermore, we find that Rictor is a haploinsufficient gene and that deleting one copy protects Pten heterozygous mice from prostate cancer. Finally, we show that the development of prostate cancer caused by Pten deletion specifically in prostate epithelium requires mTORC2, but that for normal prostate epithelial cells, mTORC2 activity is nonessential. The selective requirement for mTORC2 in tumor development suggests that mTORC2 inhibitors may be of substantial clinical utility.W. M. Keck FoundationDamon Runyon Cancer Research Foundation (Research Fellowship)Leukemia & Lymphoma Society of America (Career Development Award)Howard Hughes Medical Institute (Investigator)National Institutes of Health (U.S.) (K99 CA1296613-01A1)National Institutes of Health (U.S.) (R01 CA107166)National Institutes of Health (U.S.) (R01 AI04389)National Institutes of Health (U.S.) (R01 CA103866

Center-Extraction-Based Three Dimensional Nuclei Instance Segmentation of Fluorescence Microscopy Images

Fluorescence microscopy is an essential tool for the analysis of 3D subcellular structures in tissue. An important step in the characterization of tissue involves nuclei segmentation. In this paper, a two-stage method for segmentation of nuclei using convolutional neural networks (CNNs) is described. In particular, since creating labeled volumes manually for training purposes is not practical due to the size and complexity of the 3D data sets, the paper describes a method for generating synthetic microscopy volumes based on a spatially constrained cycle-consistent adversarial network. The proposed method is tested on multiple real microscopy data sets and outperforms other commonly used segmentation techniques