Bright field microscopy basics of investing
It was noticed that excitation with light below nm generated images with dramatically improved contrast and sharpness 10 , The second: excitation light in this same subnm spectral range can elicit bright emission from tissue specimens stained with conventional fluorescent dyes. Despite being excited in the relatively deep UV, these stains emit photons in the visible range. The visible-band signals can then be captured using simple-to-operate and inexpensive conventional glass-based microscope optics and either grayscale or colour cameras.
Fortunately, some of the dyes with this favorable excitation-emission behavior proved to label tissue components with specificities resembling those of haematoxylin and eosin. Since the excitation light is localized to within a few microns of the surface, tissue sectioning is not required for achieving a high-contrast subcellular-scale image. Tissues, either fresh or fixed, can be stained and imaged with MUSE at 3—10 frames per second within just a few minutes, compared to delays of hours or days associated with current methods.
Finally, MUSE is non-destructive, meaning that small biopsy specimens can be imaged and then submitted for additional downstream studies as necessary. Preliminary results and examples of MUSE in dermatology were recently described 13 , Based on experience to date exploring the MUSE approach, we discuss key elements of the optical design and describe the associated straightforward staining methodology.
In some instances, we show that MUSE can also generate images that contain information unobtainable using standard thin sections and brightfield microscopy. Oblique UV excitation light illuminates the specimen, bypassing the glass microscope lens, which, as it is opaque in the subnm spectral region, serves as an intrinsic excitation filter that blocks backscattered UV light from the optical path.
The oblique excitation angle, as compared to full en-face illumination, can also generate shading across the face of a specimen that usefully highlights tissue surface topography Fig. MUSE is distinct from other UV microscope systems for example 15 , 16 which detect fluorescence emission from or absorbance by thinly sectioned samples, largely in the UV spectral range; unlike MUSE, such instruments require the use of special UV-transmitting or reflecting objective lenses.
There have been numerous advancements in cell tracking e. Figure 1 Implementation overview: schematic of the data processing procedure, producing scalar cell properties from a series of low-contrast cell images pixel map stacks. Full size image In this study, we focus on establishing a set of cell properties that describe the general cell activity using human neuroblastoma SH-SY5Y cells.
To confirm the accuracy of detection in change of cell morphology and motility, we treated cells with drugs that modify the dynamics of the cytoskeletal proteins. Here, we used two drugs, cytochalasin D and taxol. Cytochalasin D is an actin polymerization inhibitor that caps the barbed end of the F-actin Previously, it has been reported that the inhibition of actin polymerization causes defects in neurite outgrowth and cell motility Taxo is well known as an anticancer drug which effects the inhibition of mitosis.
It was revealed to promote the assembly of MT Excessive stabilization of MT causes inhibition of neurite formation Further, taxol was shown to not be effective on inhibiting cell adhesion, but cell migration in various carcinoma cells.
As a result of the pharmacological treatment of actin and MTs, we superseded the changes in cell morphology and cell motility of human neuroblastoma, SH-SY5Y cells. The cell activity was suggested to be linked to the drug-concentration. As a result of the pharmacological treatment of actin and MTs using human neuroblastoma SH-SY5Y cells, cell motility was suggested to be linked to the concentration of the inhibitor We have developed a data analysis pipeline for object detection, classification and tracking in 2D gray-scale images, which is consistently written in python 3.
Next, we describe the computational cell localization and tracking approach through instance segmentation. Methods Figure 2 Image processing summary: cell detection left-hand side and cell activity estimation right-hand side. The cell detection routine computes an index map that highlights cell and contaminant locations on a pixel-by-pixel base semantic segmentation from a gray-scale image.
The cell activity estimation routine computes the various cell activity properties from a set of index images. Full size image In regards to cell detection, we have used an encoder-decoder Neural Network NN based cell detection routine which can omit large-scale contaminants without confusing them with actual cells, even in the case of superposition of cells with contaminants.
Therefore, we were able to extract labeled segmentation masks tracking each cell through a set of temporally connected image frames. Our cell detection approach is summarized on the left-hand side of Fig. Cell segmentation Figure 3 Annotation examples annotations of microscopic images for varying object densities from left to right.
The different colors represent cells - shades of purple, contaminants - green and background - black. Full size image Figure 4 Overlapping objects. Left: raw image with object contours green produced though a sobel filter and morphological operations.
Right: Annotations representing the individual annotation groups with the background in white. Full size image In our data, we have encountered three different types of image data depending on morphological sparsity sparse, dense, over-dense images as shown in Fig. The sparsity categorization is made subjectively. Images with lower sparsity are easier to segment and require less training, since only few cells are in adhesive states. However, our image data exhibits the difficulty of obstructions such as contaminants such as debris and dead cell fragments.
Those obstructions appear to be reduced in dense and over-dense cell images likely due to spatial constraints and are more likely to appear in sparse cell images. In this study, we differentiate between four annotation groups: image background, cells, contaminants and the superposition of cells and contaminants.
Since the analyzed videos exhibit a high variety in features and brightness and contrast variations, traditional filters are not sufficient to properly detect cells and differentiate them from contaminants. This is expressed in Fig. Attempting to measure cellular activity requires precise spatial and time-resolved localization of each cell within the observed field of view FOV. Therefore, we have attempted to precisely detect each cell and all its components such as protrusions, while omitting contaminants and taking into account cell-cell and cell-contaminant adhesion as well as superposition.
In this research, we use cell segmentation based on edge-enhanced instance segmentation. This approach is based on a convolutional encoder-decoder NN U-Net model. The U-Net model by itself produces semantic segmentation masks, which on its own, is not able to properly differentiate between cells inside cell groups.
Ronneberger et. Our method follows this general idea, while being implemented in python using pytorch 29 instead of caffe. Further, we used the entire cell edge and not just the edge where cells connect or are in close proximity to nudge the optimizer in order to improve the separation between cell edges of neighboring cells. The architecture of our U-Net model is similar to previous reports with a few variations in depth as well as implementation 30 , 31 , 32 , 33 , Compared to rule-based segmentation approaches 35 , encoder-decoder NN such as corrected U-Net and Cell-distance CCN have shown accurate results on specific data sets 24 , 36 , 37 , 38 , 39 , 40 , From our point of view, disadvantages of supervised training approaches are the requirement of tedious manual annotation, expensive GPUs for extended training instances, the production of outputs that is incomprehensible as well as the generation of false positives when encountering conditions, which the NN has not experience during training.
The images were randomly chosen from within the 72 time-lapse observations of the experiment as well as separately produced sample of PC12 cells, which were not analyzed in this study but used for training. As presented in Fig.
While each group is annotated within its own layer, we also assigned a new layer to connecting or overlapping cells different shades of violet in Fig. Therefore, it is possible to store information of superpositions between the individual groups. It is very important to distinguish between cells, contaminants and their superposition to properly minimize the cross-entropy loss during training. This is due to the fact, that cells and contaminants exhibit similar features that are drastically different from the background.
A detailed discussion on the component separation in cell image annotations will be elaborated in future work. We used the python package psd-tools 42 and the morphology package of scikit-image 43 and scipy 44 to correct inconsistencies from the annotation process and also read and convert the annotations into the necessary pytorch tensors containing training images and integer-labeled annotations.
We pre-processed the raw images only for better visibility but used raw images for training and inference, since the images become oversaturated and textures within cells and contaminants become very similar or indistinguishable. From the annotated samples, we produced a set of one thousand image-annotation pairs by applying random augmentation operations crop, rotation, reflection, warp distortion and swirl distortion. In addition we compute the locations where cells and contaminants overlap and add those and the cell borders as new annotations groups.
Figure 5 Full size image We then trained the U-Net model using the augmented image dataset. The dataset has been equally split into training an evaluation data, before augmentation. The loss evolution for training and validation data is presented in Fig. The learning rate was adjusted, when the loss minimization would slow down. Before training, we add two additional segmentation groups. One containing the overlap between cells and contaminants and the other containing the cell contours two pixel width through morphological operations such as erosion and dilation.
From this we compute a penalty map, which is enforced between each training cycle through an exponential amplification of the cross entropy loss at the edge position of the cell segmentation group mask. With this amplification of the edge loss, we attempt to resolve not only cell gaps but also cell borders precisely. Figure 6 Inference example of the U-Net segmentation approach.
A the probability maps for the individual segmentation groups. Full size image During inference, the probabilities for each segmentation group are computed by applying the pixel-wise soft-max function to the output of the trained model. The segmentation probabilities are presented in Fig. The segmentation map Fig. Segmentation refinement and cell tracking Our image data is occasionally contaminated with dead cells, cell debris, and dust clumps which can in single images be confused even by the trained eye with cells.
However, we have noticed that most of the contaminants move with significantly higher velocities than their cellular counterparts. However, cell count and cell density estimations require the absolute number of cells in each frame, which we have mostly omitted in this study, also because of the mostly low number of cells in the FOV. Therefore, we are able to precisely track the individual cells by comparing overlapping islands in neighboring frames 23 , Islands are denoted by values larger than zero, within the computed segmentation mask Background pixel have the value zero.
We compare each island in one frame to each island in the next frame through superposition and numerical label the individual cells by pixel area from large to small area starting with zero for the background. Superimposing islands in neighboring frames are assigned the same index number. We only include cells present during the entire observation-period.
These cells are automatically tracked and identified in every frame of the time-lapse sequence. This way we do not completely rely on the classification accuracy of the classification routine, embedded within U-Net contour of Fig. Figure 7 Cell tracking and segmentation refinement the upper sequence, shows the result from our primary segmentation and tracking approach via overlapping labels in neighboring frames.
From local maxima within the distance map, we compute the distribution of additional cell vertices. While each frame within the sequence shows three cells, either only one or two are correctly labeled. The center sequence: additional cell vertices are computed by instances where cells labels disconnect.
In the lower sequence: cells are differentiated through watershed segmentation based on the cell label and the cell vertices. Full size image In addition, we identified the individual components of a cell cluster if they separate at one or more instances during the observation through watershed-enhanced cell tracking at the instance of adhesion. Here the identified patches are separated based on additional possible nucleus location within each patch. Here, we compute generate the additional nucleus candidates by computing the local maxima within the distance map of each patch.
Temporally disconnected nuclei are then matched to nucleus candidates in neighboring frames via nearest neighbor search. Patch separation in both time directions is presented in Fig. In general, this approach has previously been implemented and presented by Jia et. We have estimated the mean Average Precision of the reference data.
Estimation of cell morphology and migration Figure 8 Cell properties. Descriptive properties for cell migration are cell velocity derived from the cell vertex and directional persistence. Full size image The aim of our study was to formulate a reproducible measure for physical cell responses in microscopic images. We differentiated between two kinds of cell activity: translational and morphological activity and their various degrees of freedom, as presented in Fig.
Furthermore, we also compute the time derivatives of the presented cell properties. In this section we introduce the derivation and computational approach for the individual cell property parameter starting with the morphological properties. Cell morphology and its dynamics After successfully isolating individual cells from each other and the background by assigning index numbers to each pixel, it is possible to compute the area as sum of all pixels with the same index number.
In this regard r is the distance from the origin to the curve element for each cell with index n. In this regard, shape complexity can roughly be evaluated by comparing area and perimeter. However, In this study the cells are of the same species and are of very similar size, where cell shapes resembling very elongated ellipses are very much uncommon.
While the direction-dependent morphology varies, the position of the weighted center of mass also varies. Full size image The distance map of object masks as previously presented by 47 and 15 are useful tools to compute morphological cell properties such as cell vertex locations, protrusion properties and cell eccentricity vectors. The distance map is a representation of the input map, where each pixel value represents its shortest distance to the mask edge.
The euclidean distance map and the cell vertex position is computed from the global maximum position of each cell mask. To determine cell migrative properties, it is imperative to find a well-defined cell center. A systematic analysis of the weighted center of mass in comparison to prior approaches will be discussed in future research. For angular cell perimeter measurements 47 and 15 use an erosion-based approach, which introduces additional parameters that require fine-tuning by hand.
This is in return very accurate for finding complex substructures within protrusions and filopodia. However, the cells in our images only exhibit first order protrusions without multiple branching. Cell translational dynamics In this section, we will briefly describe the translational properties, which we computed to determine the migrative cell activity components.
Results Figure 12 Summary of cell activity. Radar charts summarizing the cellular response of SH-SY5Y cells to the exposure of various concentrations of cytochalasin D and taxol. Full size image We present a semi-automated reduction pipeline to extract cells and their morphological as well as translational properties from two-dimensional, low-contrast gray-scale time-series observations bright-field images. We have analyzed 72 video files, each consisting of frames each.
In this section, we present the results of the above-mentioned measures for cell activity for three sets of SH-SY5Y cell cultures exposed to individual inhibitors of cytochalasin D, taxol as well as a combination of both and compare the results to the literature. Graphical summaries in the form of radar charts for several concentrations of the above-mentioned inhibitors are presented in Fig.
Figure 13 Cell property distribution per cell and per frame for six cell properties. For each cell property, we present the corresponding distribution for all cells within the FOV for the control samples, cytochalasin D top as well as taxol middle and cytochalasin D combined with taxol bottom.
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