7,631 research outputs found
Automatic Recognition of Light Microscope Pollen Images
This paper is a progress report on a project aimed at the realization of a low-cost, automatic, trainable system "AutoStage" for recognition and counting of pollen. Previous work on image feature selection and classification has been extended by design and integration of an XY stage to allow slides to be scanned, an auto focus system, and segmentation software. The results of a series of classification tests are reported, and verified by comparison with classification performance by expert palynologists. A number of technical issues are addressed, including pollen slide preparation and slide sampling protocols
Technical note : TRACKFlow, a new versatile microscope system forfission track analysis
We here present TRACKFlow, a new system with dedicated modules for the fission track (FT) laboratory. It is based on the motorised Nikon Eclipse Ni-E upright microscope with the Nikon DS-Ri2 full frame camera and is embedded within the Nikon NIS-Elements Advanced Research software package. TRACKFlow decouples image acquisition from analysis to decrease schedule stress of the microscope. The system further has the aim of being versatile, adaptable to multiple preparation protocols and analysis approaches. It is both suited for small-scale laboratories and is also ready for upscaling to high-throughput imaging. The versatility of the system, based on the operators’ full access to the NIS-Elements package, exceeds that of other systems for FT and further expands to stepping away from the dedicated FT microscope towards a general microscope for Earth Sciences, including dedicated modules for FT research.
TRACKFlow consists of a number of user-friendly protocols which are based on the well plate design that allows sequential scanning of multiple samples without the need of replacing the slide on the stage. All protocols include a sub-protocol to scan a map of the mount for easy navigation through the samples on the stage. Two protocols are designed for the External Detector Method (EDM) and the LA–ICP–MS apatite fission track (LAFT) approach, with tools for repositioning and calibration to the external detector. Two other tools are designed for large crystals, such as the Durango age standard and U-doped glass external detectors. These protocols generate a regular grid of points and inspect if each point is suitable for analysis. Both protocols also include an option to image each withheld point. One more protocol is included for the measurement of etch pit diameters and one last protocol prepares a list of coordinates for correlative microscopy. In a following phase of development TRACKFlow can be expanded towards fully autonomous calibration, grain detection and imaging
An Automated System for Chromosome Analysis
The design, construction, and testing of a complete system to produce karyotypes and chromosome measurement data from human blood samples, and to provide a basis for statistical analysis of quantitative chromosome measurement data are described
Hardware and software integration and testing for the automation of bright-field microscopy for tuberculosis detection
Automated microscopy for the detection of tuberculosis (TB) in sputum smears would reduce the load on technicians, especially in countries with a high TB burden. This dissertation reports on the development and testing of an automated system built around a conventional microscope for the detection of TB in Ziehl-Neelsen (ZN) stained sputum smears. Microscope auto-focusing, image analysis and stage movement were integrated. Images were captured at 40x magnification
Computational illumination for high-speed in vitro Fourier ptychographic microscopy
We demonstrate a new computational illumination technique that achieves large
space-bandwidth-time product, for quantitative phase imaging of unstained live
samples in vitro. Microscope lenses can have either large field of view (FOV)
or high resolution, not both. Fourier ptychographic microscopy (FPM) is a new
computational imaging technique that circumvents this limit by fusing
information from multiple images taken with different illumination angles. The
result is a gigapixel-scale image having both wide FOV and high resolution,
i.e. large space-bandwidth product (SBP). FPM has enormous potential for
revolutionizing microscopy and has already found application in digital
pathology. However, it suffers from long acquisition times (on the order of
minutes), limiting throughput. Faster capture times would not only improve
imaging speed, but also allow studies of live samples, where motion artifacts
degrade results. In contrast to fixed (e.g. pathology) slides, live samples are
continuously evolving at various spatial and temporal scales. Here, we present
a new source coding scheme, along with real-time hardware control, to achieve
0.8 NA resolution across a 4x FOV with sub-second capture times. We propose an
improved algorithm and new initialization scheme, which allow robust phase
reconstruction over long time-lapse experiments. We present the first FPM
results for both growing and confluent in vitro cell cultures, capturing videos
of subcellular dynamical phenomena in popular cell lines undergoing division
and migration. Our method opens up FPM to applications with live samples, for
observing rare events in both space and time
Field-portable pixel super-resolution colour microscope.
Based on partially-coherent digital in-line holography, we report a field-portable microscope that can render lensfree colour images over a wide field-of-view of e.g., >20 mm(2). This computational holographic microscope weighs less than 145 grams with dimensions smaller than 17×6×5 cm, making it especially suitable for field settings and point-of-care use. In this lensfree imaging design, we merged a colorization algorithm with a source shifting based multi-height pixel super-resolution technique to mitigate 'rainbow' like colour artefacts that are typical in holographic imaging. This image processing scheme is based on transforming the colour components of an RGB image into YUV colour space, which separates colour information from brightness component of an image. The resolution of our super-resolution colour microscope was characterized using a USAF test chart to confirm sub-micron spatial resolution, even for reconstructions that employ multi-height phase recovery to handle dense and connected objects. To further demonstrate the performance of this colour microscope Papanicolaou (Pap) smears were also successfully imaged. This field-portable and wide-field computational colour microscope could be useful for tele-medicine applications in resource poor settings
An automated system for chromosome analysis. Volume 1: Goals, system design, and performance
The design, construction, and testing of a complete system to produce karyotypes and chromosome measurement data from human blood samples, and a basis for statistical analysis of quantitative chromosome measurement data is described. The prototype was assembled, tested, and evaluated on clinical material and thoroughly documented
Robust particle outline extraction and its application to digital on-line holography
Peer reviewedPostprin
iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope
Artificial intelligence; Malaria diagnosis; Robotized microscopeInteligencia artificial; Diagnóstico de malaria; Microscopio robotizadoIntel·ligència artificial; Diagnòstic de malà ria; Microscopi robotitzatIntroduction: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it.
Methods: In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide.
Results: Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application.
Conclusion: The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.The project is funded by the Microbiology Department of Vall d’Hebron University Hospital, the Cooperation Centre of the Universitat Politècnica de Catalunya (CCD-UPC), and the Probitas Foundation
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