Adaptive Cell Segmentation and Tracking for Volumetric Confocal Microscopy Images of a Developing Plant Meristem

Automated segmentation and tracking of cells in actively developing tissues can provide high-throughput and quantitative spatiotemporal measurements of a range of cell behaviors; cell expansion and cell-division kinetics leading to a better understanding of the underlying dynamics of morphogenesis. Here, we have studied the problem of constructing cell lineages in time-lapse volumetric image stacks obtained using Confocal Laser Scanning Microscopy （CLSM）. The novel contribution of the work lies in its ability to segment and track cells in densely packed tissue, the shoot apical meristem （SAM）, through the use of a close-loop, adaptive segmentation, and tracking approach. The tracking output acts as an indicator of the quality of segmentation and, in turn, the segmentation can be improved to obtain better tracking results. We construct an optimization function that minimizes the segmentation error, which is, in turn, estimated from the tracking results. This adaptive approach significantly improves both tracking and segmentation when compared to an open loop framework in which segmentation and tracking modules operate separately.

Cellbow:a robust customizable cell segmentation program

Backgrounds Time-lapse live cell imaging of a growing cell population is routine in many biological investigations.A major challenge in imaging analysis is accurate segmentation,a process to define the boundaries of cells based on raw image data.Current segmentation methods relying on single boundary features have problems in robustness when dealing with inhomogeneous foci which invariably happens in cell population imaging.Methods:Combined with a multi-layer training set strategy,we developed a neural-network-based algorithm—Cellbow.Results'Cellbow can achieve accurate and robust segmentation of cells in broad and general settings.It can also facilitate long-term tracking of cell growth and division.To facilitate the application of Cellbow,we provide a website on which one can online test the software,as well as an I mage J plugin for the user to visualize the performance before software installation.Conclusions Cellbow is customizable and generalizable.It is broadly applicable to segmenting fluorescent images of diverse cell types with no further training needed.For bright-field images,only a small set of sample images of the specific cell type from the user may be needed for training.

A new level set model for cell image segmentation

In this paper we first determine three phases of cell images： background, cytoplasm and nucleolus according to the general physical characteristics of cell images, and then develop a variational model, based on these characteristics, to segment nucleolus and cytoplasm from their relatively complicated backgrounds. In the meantime, the preprocessing obtained information of cell images using the OTSU algorithm is used to initialize the level set function in the model, which can speed up the segmentation and present satisfactory results in cell image processing.

Effective Virtual Reality Based Building Navigation Using Dynamic Loading and Path Optimization

Although computer capabilities have been improved significantly, a large-scale virtual reality （VR） system demands much more in terms of memory and computation than the current computer systems can offer. This paper discusses two important issues related to VR performance and applications in building navigation. These are dynamic loading of models based on cell segmentation for the optimal VR operation, and the route optimization based on path planning for easy navigation. The VR model of engineering and information technology complex （EITC） building at the University of Manitoba is built as an example to show the feasibility of the proposed methods. The reality, enhanced by three-dimensional （3D） real-time interactivity and visualization, leads navigators into a state of the virtual building immersion.

Advancing biological fluorescence microscopy with deep learning:a bibliometric perspective

Background:Fluorescence microscopy has increasingly promising applications in life science.This bibliometrics-based review focuses on deep learning assisted fluorescence microscopy imaging techniques.Methods:Papers on this topic retrieved by Core Collection on Web of Science between 2017 and July 2022 were used for the analysis.In addition to presenting the representative papers that have received the most attention,the process of development of the topic,the structure of authors and institutions,the selection of journals,and the keywords are analyzed in detail in this review.Results:The analysis found that this topic gained immediate popularity among scholars from its emergence in 2017,gaining explosive growth within three years.This phenomenon is because deep learning techniques that have been well established in other fields can be migrated to the analysis of fluorescence micrographs.From 2020 onwards,this topic tapers off but has attracted a few stable research groups to tackle the remaining challenges.Although this topic has been very popular,it has not attracted scientists from all over the world.The USA,China,Germany,and the UK are the key players in this topic.Keyword analysis and clustering are applied to understand the different focuses on this topic.Conclusion:Based on the bibliometric analysis,the current state of this topic to date and future perspectives are summarized at the end.

Justification of Lymphotropic Injection of An Immunomodulator in Odontogenic Osteomyelitis

The lymphotropic injection of the drug named “Gamma-plant", which is an extract of potato sprouts(Solanituberosigerminum extract) and has a high therapeutic effect, was proved by pathomorphological studies in the experiment onguinea pigs (n = 18).

Experimental Investigation on Local Behaviors of PEMFC with Segmented Cell

When designing a cell stack and developing an operational strategy for proton exchange membrane fuel cell,it is critical to characterize the local current,water and heat.To measure distributions of current density,relative humidity and temperature for both anode and cathode simultaneously along the straight parallel flow channels,this paper uses a segmented tool based on the multilayered printed circuit board flow field plates with embedded sensors.In this study,two kinds of experimental operations of fuel cell reactants are carried out for comparison:the co-flow operation with identical gas flow direction of hydrogen and air and the counter-flow operation with opposite gas flow directions.The detected relative humidity(RH)distributions of both anode and cathode indicate that the asymmetry of RH distribution at two sides of the membrane in counter-flow operation is better at holding water inside the fuel cell compared with the co-flow operation.The in situ measured performance distributions show that segments around the middle of the fuel cell contribute the highest current in counter-flow operation,while for co-flow operation,the current peak locates near the outlet of reactants.

Hybrid model enabling highly efficient follicular segmentation in thyroid cytopathological whole slide image

Background The prevalence of thyroid cancer is growing rapidly.Early and precise diagnosis is critical in thy-roid cancer caring.An automatic thyroid cancer diagnostic tool can be valuable to achieve early detection and diagnostic consistency.Only the follicular areas in the sample contain useful information to the thyroid cancer diagnosis based on fine needle aspiration(FNA).This study aimed to develop a highly efficient accurate method for follicular cell areas segmentation(FCAS)of thyroid cytopathological whole slide images(WSIs).Methods A total of 96 cell samples from July 2017 to July 2018 were collected in one hospital in Beijing,China.Forty-three WSIs were selected and manually labeled,including 17 cases of papillary thyroid carci-noma sample and 26 cases of benign sample.Six thousand and nine hundred cropped typical image patches(available on https://github.com/bupt-ai-cz/Hybrid-Model-Enabling-Highly-Efficient-Follicular-Segmentation)of 1024×1024 pixels from 13 large WSIs were used for patch-level model training and testing and all of the 13 large WSIs were papillary thyroid carcinoma samples.Thirty testing WSIs with an average size 36,217×29,400(from 10,240×10,240 to 81,920×61,440)were used to test the effectiveness of the hybrid model.Based on the traditional semantic segmentation model deeplabv3,we constructed a hybrid segmentation architecture by adding a classification branch into the segmentation scheme to improve efficiency.Accuracy was used to measure the performance of the classification model;pixel accuracy(pAcc),mean accuracy(mAcc),mean intersection over union(mIoU),and frequency weighted intersection over union(fwIoU)were used to measure the performance of the segmentation model,respectively.Results Using this method,up to 93%WSI segmentation time was reduced by skipping the colloidal areas and the blank background areas.The average processing time of 30 WSI was 49.49 s.On the patch dataset,this hybrid model might reach pAcc=98.65%,mAcc=85.60%,mIoU=79.61%,and fwIoU=97.54%.On the WSI dataset,this model might reach pAcc=99.30%,mAcc=68.94%,mIoU=58.21%,and fwIoU=99.50%.Conclusion The proposed hybrid method might significantly improve previous solutions and achieve the superior performance of efficiency and accuracy.