An Open-Source Whole Slide Image Registration Workflow at Cellular Precision Using Fiji, QuPath and Elastix

Image analysis workflows for Histology increasingly require the correlation and combination of measurements across several whole slide images. Indeed, for multiplexing, as well as multimodal imaging, it is indispensable that the same sample is imaged multiple times, either through various systems for multimodal imaging, or using the same system but throughout rounds of sample manipulation (e.g. multiple staining sessions). In both cases slight deformations from one image to another are unavoidable, leading to an imperfect superimposition Redundant and thus a loss of accuracy making it difficult to link measurements, in particular at the cellular level. Using pre-existing software components and developing missing ones, we propose a user-friendly workflow which facilitates the nonlinear registration of whole slide images in order to reach sub-cellular resolution level. The set of whole slide images to register and analyze is at first defined as a QuPath project. Fiji is then used to open the QuPath project and perform the registrations. Each registration is automated by using an elastix backend, or semi-automated by using BigWarp in order to interactively correct the results of the automated registration. These transformations can then be retrieved in QuPath to transfer any regions of interest from an image to the corresponding registered images. In addition, the transformations can be applied in QuPath to produce on-the-fly transformed images that can be displayed on top of the reference image. Thus, relevant data can be combined and analyzed throughout all registered slides, facilitating the analysis of correlative results for multiplexed and multimodal imaging.

Design and Implementation of a Software-Based Advance Electronic Voting Machine Using Automatic Registration and Fingerprint Identification

In democratic countries, free and fair elections are required to quantify the populace's sentiments to form a government of representatives. It is challenging to maneuver due to the procedural variation from country to country and complexity. As paper-based electoral systems are slow and prone to error that take hours and ample manpower to announce the results, thus a secure efficient electoral system is always preferred. In this paper, we have proposed a secure implementation of auto-registration fingerprint identification-based electronic voting systems to overcome the aspect of accuracy and transparency. We have included a novel feature of automated registration to authenticate the user through identity before the vote casting. Moreover, credentials of voters are collected in a database including fingerprints, and communication of encrypted data between server and machine with secured Secure Socket Layer (SSL). Additionally, the voter can cast their votes through a touch screen Graphical User Interface (GUI), and once the voting time end, the screen can automatically disappear by authorizing admin to print Form-45. Conclusively, the proposed system count votes automatically that is much faster and accurate than the traditional voting techniques. Moreover, the results will be available to the general public in 1-2 hours which ensures fair elections.

Automatic Toll Collection System and Detection of Theft Vehicle by using RFID

The road transportation in India has growing day by day. The Indian road network carries about 65 percent of its Goods and about 85 percent of passengers. The national highways in India are now increased from 44,076 miles to 88,313 miles in 2018 to 2019. Along these road networks there are about 450 toll plazas totally in India. These toll plazas contribute some major part of India’s economy, it is estimated the toll revenue can swell up to 1 lakh crore rupees in next five years. In spite of this enormous growth the toll plazas have huge traffic during peak most of the time which incurs delay and loss human hours to surpass the toll. The proposed system focuses on an automatic toll collection system which will automatically debit the amount from vehicle owner’s bank. The automated registration and collection are based on using the RFID chip which is amalgamated in the vehicle’s windshield. By this method, the information about registration ID of the vehicle can be obtained therefore stolen vehicle can also be detected by this system as the vehicle ID is linked to the user’s account. Thus, this system provides fast and efficient surpassing of toll plazas without any delay.

IBEX: A versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues

The diverse composition of mammalian tissues poses challenges for understanding the cell–cell interactions required for organ homeostasis and how spatial relationships are perturbed during disease. Existing methods such as single-cell genomics, lacking a spatial context, and traditional immunofluorescence, capturing only two to six molecular features, cannot resolve these issues. Imaging technologies have been developed to address these problems, but each possesses limitations that constrain widespread use. Here we report a method that overcomes major impediments to highly multiplex tissue imaging. “Iterative bleaching extends multiplexity” (IBEX) uses an iterative staining and chemical bleaching method to enable high-resolution imaging of >65 parameters in the same tissue section without physical degradation. IBEX can be employed with various types of conventional microscopes and permits use of both commercially available and user-generated antibodies in an “open” system to allow easy adjustment of staining panels based on ongoing marker discovery efforts. We show how IBEX can also be used with amplified staining methods for imaging strongly fixed tissues with limited epitope retention and with oligonucleotide-based staining, allowing potential cross-referencing between flow cytometry, cellular indexing of transcriptomes and epitopes by sequencing, and IBEX analysis of the same tissue. To facilitate data processing, we provide an open-source platform for automated registration of iterative images. IBEX thus represents a technology that can be rapidly integrated into most current laboratory workflows to achieve high-content imaging to reveal the complex cellular landscape of diverse organs and tissues.

DeepCLEM: automated registration for correlative light and electron microscopy using deep learning

In correlative light and electron microscopy (CLEM), the fluorescent images must be registered to the EM images with high precision. Due to the different contrast of EM and fluorescence images, automated correlation-based alignment is not directly possible, and registration is often done by hand using a fluorescent chromatin stain, or semi-automatically with fiducial markers. We introduce “DeepCLEM”, a fully automated CLEM registration workflow. A convolutional neural network predicts the fluorescent signal from the EM images, which is then automatically registered to the experimentally measured chromatin signal from the sample using correlation-based alignment. The complete workflow is available as a FIJI macro and could in principle be adapted for other imaging modalities as well as for 3D stacks.