Sub-micro scale cell segmentation using deep learning.

Automated cell segmentation is key for rapid and accurate investigation of cell responses. As instrumentation resolving power increases, clear delineation of newly revealed cellular features at the submicron through nanoscale becomes important. Reliance on the manual investigation of myriad small features retards investigation; however, use of deep learning methods has great potential to reveal cell features both at high accuracy and high speed, which may lead to new discoveries in the near term. In this study, semantic cell segmentation systems were investigated by implementing fully convolutional neural networks called U-nets for the segmentation of astrocytes cultured on poly-l-lysine-functionalized planar glass. The network hyperparameters were determined by changing the number of network layers, loss functions, and input image modalities. Atomic force microscopy (AFM) images were selected for investigation as these are inherently nanoscale and are also dimensional. AFM height, deflection, and friction images were used as inputs separately and together, and the segmentation performances were investigated on five-fold cross-validation data. Transfer learning methods, including VGG16, VGG19, and Xception, were used to improve cell segmentation performance. We find that AFM height images inherit more discriminative features than AFM deflection and AFM friction images for cell segmentation. When transfer-learning methods are applied, statistically significant segmentation performance improvements are observed. Segmentation performance was compared to classical image processing algorithms and other algorithms in use by considering both AFM and electron microscopy segmentation. An accuracy of 0.9849, Matthews correlation coefficient of 0.9218, and Dice's similarity coefficient of 0.9306 were obtained on the AFM test images. Performance evaluations show that the proposed system can be successfully used for AFM cell segmentation with high precision.

A novel quantitative volumetric spreading index definition and assessment of astrocyte spreading in vitro.

A novel quantitative volumetric spreading index (VSI) is defined that depends on the total distance between object voxels and the contact surface plane in three-dimensional (3D) space. The VSI, which ranges from 0 to 1, is rotationally invariant around the z-axis. VSI can be used to quantify the degree of individual cell spreading, which is important for analysis of cell interactions with their environment. The VSIs of astrocytes cultured on a nanofibrillar surface and three different comparative planar surfaces have been calculated from confocal laser scanning microscope z-series images, and the effects of both culture surface and immunoreactivity on the degree of cell spreading were investigated. VSI calculations indicated a statistical correlation between increased reactivity, based on immunolabeling for glial fibrillary acidic protein, and decreased cell spreading. Further results provided a quantitative measure for the increased spreading of quiescent-like and reactive-like astrocytes on planar substrates functionalized with poly-l-lysine. © 2017 International Society for Advancement of Cytometry.

Texture-based segmentation and a new cell shape index for quantitative analysis of cell spreading in AFM images.

A new cell shape index is defined for use with atomic force microscopy height images of cell cultures. The new cell shape index reveals quantitative cell spreading information not included in a conventional cell shape index. A supervised learning-based cell segmentation algorithm was implemented by texture feature extraction and a multi-layer neural network classifier. The texture feature sets for four different culture surfaces were determined from the gray level co-occurrence matrix and local statistics texture models using two feature selection algorithms and by considering computational cost. The quantitative morphometry of quiescent-like and reactive-like cerebral cortical astrocytes cultured on four different culture environments was investigated using the new and conventional cell shape index. Inclusion of cell spreading with stellation information through use of the new cell shape index was shown to change biomedical conclusions derived from conventional cell shape analysis based on stellation alone. The new CSI results showed that the quantitative astrocyte spreading and stellation behavior was induced by both the underlying substrate and the immunoreactivity of the astrocytes.

Differentiation of reactive-like astrocytes cultured on nanofibrillar and comparative culture surfaces.

AIM: To investigate the directive importance of nanophysical properties on the morphological and protein expression responses of dibutyryladenosine cyclic monophosphate (dBcAMP)-treated cerebral cortical astrocytes in vitro. MATERIALS & METHODS: Elasticity and work of adhesion characterizations of culture surfaces were performed using atomic force microscopy and combined with previous surface roughness and polarity results. The morphological and biochemical differentiation of dBcAMP-treated astrocytes cultured on promising nanofibrillar scaffolds and comparative culture surfaces were investigated by immunocytochemistry, colocalization, super resolution microscopy and atomic force microscopy. The dBcAMP-treated astrocyte responses were further compared with untreated astrocyte responses. RESULTS & CONCLUSION: Nanofibrillar scaffold properties were shown to reduce immunoreactivity responses while poly-L-lysine-functionalized Aclar® (Ted Pella Inc., CA, USA) properties were shown to induce responses reminiscent of glial scar formation. The comparison study indicated that directive cues may differ in wound-healing versus quiescent situations.

Nanofibrillar scaffolds induce preferential activation of Rho GTPases in cerebral cortical astrocytes.

Cerebral cortical astrocyte responses to polyamide nanofibrillar scaffolds versus poly-L-lysine (PLL)-functionalized planar glass, unfunctionalized planar Aclar coverslips, and PLL-functionalized planar Aclar surfaces were investigated by atomic force microscopy and immunocytochemistry. The physical properties of the cell culture environments were evaluated using contact angle and surface roughness measurements and compared. Astrocyte morphological responses, including filopodia, lamellipodia, and stress fiber formation, and stellation were imaged using atomic force microscopy and phalloidin staining for F-actin. Activation of the corresponding Rho GTPase regulators was investigated using immunolabeling with Cdc42, Rac1, and RhoA. Astrocytes cultured on the nanofibrillar scaffolds showed a unique response that included stellation, cell-cell interactions by stellate processes, and evidence of depression of RhoA. The results support the hypothesis that the extracellular environment can trigger preferential activation of members of the Rho GTPase family, with demonstrable morphological consequences for cerebral cortical astrocytes.

AFM feature definition for neural cells on nanofibrillar tissue scaffolds.

A diagnostic approach is developed and implemented that provides clear feature definition in atomic force microscopy (AFM) images of neural cells on nanofibrillar tissue scaffolds. Because the cellular edges and processes are on the same order as the background nanofibers, this imaging situation presents a feature definition problem. The diagnostic approach is based on analysis of discrete Fourier transforms of standard AFM section measurements. The diagnostic conclusion that the combination of dynamic range enhancement with low-frequency component suppression enhances feature definition is shown to be correct and to lead to clear-featured images that could change previously held assumptions about the cell-cell interactions present. Clear feature definition of cells on scaffolds extends the usefulness of AFM imaging for use in regenerative medicine.

Nanopipes in gallium nitride nanowires and rods.

Gallium nitride nanowires and rods synthesized by a catalyst-free vapor-solid growth method were analyzed with cross section high-resolution transmission electron microscopy. The cross section studies revealed hollow core screw dislocations, or nanopipes, in the nanowires and rods. The hollow cores were located at or near the center of the nanowires and rods, along the axis of a screw dislocation. The formation of the hollow cores is consistent with effect of screw dislocations with giant Burgers vector predicted by Frank.

Cell classification by moments and continuous wavelet transform methods.

Image processing techniques are bringing new insights to biomedical research. The automatic recognition and classification of biomedical objects can enhance work efficiency while identifying new inter-relationships among biological features. In this work, a simple rule-based decision tree classifier is developed to classify typical features of mixed cell types investigated by atomic force microscopy (AFM). A combination of continuous wavelet transform (CWT) and moment-based features are extracted from the AFM data to represent that shape information of different cellular objects at multiple resolution levels. The features are shown to be invariant under operations of translation, rotation, and scaling. The features are then used in a simple rule-based classifier to discriminate between anucleate versus nucleate cell types or to distinguish cells from a fibrous environment such as a tissue scaffold or stint. Since each feature has clear physical meaning, the decision rule of this tree classifier is simple, which makes it very suitable for online processing. Experimental results on AFM data confirm that the performance of this classifier is robust and reliable.

Electronic and structural characteristics of zinc-blende wurtzite biphasic homostructure GaN nanowires.

We report a new biphasic crystalline wurtzite/zinc-blende homostructure in gallium nitride nanowires. Cathodoluminescence was used to quantitatively measure the wurtzite and zinc-blende band gaps. High-resolution transmission electron microscopy was used to identify distinct wurtzite and zinc-blende crystalline phases within single nanowires through the use of selected area electron diffraction, electron dispersive spectroscopy, electron energy loss spectroscopy, and fast Fourier transform techniques. A mechanism for growth is identified.

Scanning probe recognition microscopy investigation of tissue scaffold properties.

Scanning probe recognition microscopy is a new scanning probe microscopy technique which enables selective scanning along individual nanofibers within a tissue scaffold. Statistically significant data for multiple properties can be collected by repetitively fine-scanning an identical region of interest. The results of a scanning probe recognition microscopy investigation of the surface roughness and elasticity of a series of tissue scaffolds are presented. Deconvolution and statistical methods were developed and used for data accuracy along curved nanofiber surfaces. Nanofiber features were also independently analyzed using transmission electron microscopy, with results that supported the scanning probe recognition microscopy-based analysis.