7 research outputs found
A Deep Learning Model for Automated Segmentation of Fluorescence Cell images
Deep learning techniques bring together key advantages in biomedical image
segmentation. They speed up the process, increase the reproducibility, and reduce the workload
in segmentation and classification. Deep learning techniques can be used for analysing cell
concentration, cell viability, as well as the size and form of each cell. In this study, we develop
a deep learning model for automated segmentation of fluorescence cell images, and apply it
to fluorescence images recorded with a home-built epi-fluorescence microscope. A deep neural
network model based on U-Net architecture was built using a publicly available dataset of
cell nuclei images [1]. A model accuracy of 97.3% was reached at the end of model training.
Fluorescence cell images acquired with our home-built microscope were then segmented using
the developed model. 141 of 151 cells in 5 images were successfully segmented, revealing a
segmentation success rate of 93.4%. This deep learning model can be extended to the analysis
of different cell types and cell viability
An LED-Based Structured Illumination Microscope Using A Digital Micromirror Device And GPU Accelerated Image Reconstruction
When combined with computational approaches, fluorescence imaging becomes one of the
most powerful tools in biomedical research. It is possible to achieve resolution figures
beyond the diffraction limit, and improve the performance and flexibility of high-resolution
imaging systems with techniques such as structured illumination microscopy (SIM) reconstruction. In this study, the hardware and software implementation of an LED-based superresolution imaging system using SIM employing GPU accelerated parallel image reconstruction is presented. The sample is illuminated with two-dimensional sinusoidal patterns
with various orientations and lateral phase shifts generated using a digital micromirror
device (DMD). SIM reconstruction is carried out in frequency space using parallel CUDA
kernel functions. Furthermore, a general purpose toolbox for the parallel image reconstruction algorithm and an infrastructure that allows all users to perform parallel operations on
images without developing any CUDA kernel code is presented. The developed image
reconstruction algorithm was run separately on a CPU and a GPU. Two different SIM reconstruction algorithms have been developed for the CPU as mono-thread CPU algorithm and
multi-thread OpenMP CPU algorithm. SIM reconstruction of 1024 × 1024 px images was
achieved in 1.49 s using GPU computation, indicating an enhancement by *28 and *20 in
computation time when compared with mono-thread CPU computation and multi-thread
OpenMP CPU computation, respectively
Fabrication and Characterization of Large Numerical Aperture, High-Resolution Optical Fiber Bundles Based on High-Contrast Pairs of Soft Glasses for Fluorescence Imaging
Fabrication and characterization of flexible optical fiber bundles (FBs) with inhouse
synthesized high-index and low-index thermally matched glasses are presented. The
FBs composed of around 15000 single-core fibers with pixel sizes between 1.1 and 10 μm are
fabricated using the stack-and-draw technique from sets of thermally matched zirconiumsilicate
ZR3, borosilicate SK222, sodium-silicate K209, and F2 glasses. With high refractive
index contrast pair of glasses ZR3/SK222 and K209/F2, FBs with numerical apertures (NAs)
of 0.53 and 0.59 are obtained, respectively. Among the studied glass materials, ZR3, SK222,
and K209 are in-house synthesized, while F2 is commercially acquired. Seven different FBs
with varying pixel sizes and bundle diameters are characterized. Brightfield imaging of a
micro-ruler and a Convallaria majalis sample and fluorescence imaging of a dye-stained
paper tissue and a cirrhotic mice liver tissue are demonstrated using these FBs, demonstrating
their good potential for microendoscopic imaging. Brightfield and fluorescence imaging
performance of the studied FBs are compared. For both sets of glass compositions, good
imaging performance is observed for FBs, with core diameter and core-to-core distance
values larger than 1.6 μm and 2.3 μm, respectively. FBs fabricated with K209/F2 glass pairs
revealed better performance in fluorescence imaging due to their higher NA of 0.59
An LED-based super resolution GPU implemented structured illumination microscope
Fluorescence imaging of sub-cellular structures with sizes below the di raction limit is vital in understanding cellular
processes. Relying on exciting the sample with di erent illumination patterns and image processing for the
elimination of background
uorescence, Structured Illumination Microscopy (SIM) provides imaging capability
beyond di raction limit using relatively simple optical setups. Here, we present a laser-free, DLP projector-based,
and GPU-implemented SIM super resolution microscope. Sub-di ractive biological structures were imaged with
a lateral resolution of 150 nm. The microscopy system is LED-based and entirely home-built which enables
customizable operation at a low cost
Image Reconstruction in Frequency Space using Sinusoidal Illumination Patterns
Yapılandırılmı¸s Aydınlatma Desenleri (Structured
Illumination Patterns) mikroskobik görüntülemede, kırımın limitinin
(diffraction limit) a¸sılarak süper çözünürlüklü görüntünün
elde edilmesini sa˘glamak için kullanılan bir görüntüleme tekni-
˘gidir. Mikroskobik görüntülemede, görüntülenmek istenen örnek
üzerine yansıtılan ı¸sık iki boyutlu sinüs aydınlatma desenleri
¸seklinde modüle edilir ve ham görüntünün elde edilmesi sa˘glanır.
Bu teknik kullanılarak ham görüntülere frekans uzayında
uygulanan görüntü yenide yapılandırma algoritması ile nihai
görüntünün çözünürlü˘günün iki kata kadar artması sa˘glanmı¸s
olur. Bu çalı¸smada, öncelikle yüksek çözünürlüklü hedef görüntünün
elde edilmesi için bir test görüntüsü yapılandırılmı¸s
aydınlatma desenleri (iki boyutlu sinüzoidal görüntü olarak) ile
moire giri¸sim deseni olu¸sturacak ¸sekilde konvolüsyon çarpımına
u˘gratılmı¸stır.Daha sonra yapılandırılmı¸s aydınlatma mikroskobisi
tekni˘gi algoritmasının adımları anlatılmı¸stır. Son olarak frekans
uzayında görüntü yeniden yapılandırma (image reconstruction)
için gerekli algoritma geli¸stirilmi¸s ve sonuçlar gösterilmi¸stir.Structured Illumination Patterns is an imaging
technique used in microscopic imaging to achieve super-resolution
image by exceeding the diffraction limit. In microscopic imaging,
the light projected onto the sample to be imaged is modulated
into two dimensional sinusoidal illumination patterns and the raw
image is obtained. By using this technique, the image reconstruction
algorithm applied to the raw images in the frequency space
is provided to increase the resolution of the final image up to two
times. In this study, to obtain the high resolution target image,
convolution multiplication of the structured illumination patterns
with a test image is applied and a moire fringe pattern is formed
as a result of this product. Next, the steps of the structured illumination
microscopy technique algorithm are described. Finally,
the algorithm for image reconstruction in frequency space has
been developed and the results are shown
Three-dimensional imaging of cleared human liver tissues reveals extensive fibrosis heterogeneity in non-alcoholic fatty liver disease
Background and aims: Liver fibrosis is a dynamic process of fibrogenesis and fibrolysis traditionally evaluated by liver biopsy. Accurate assessment of hepatic fibrosis is vital for optimal biomarker and drug development in the era of NASH clinical trials. The limitations of biopsy-based fibrosis staging include semi-quantitative assessment, sampling, and observer variability. In this study, we aimed to perform 3D imaging of optically transparent whole liver biopsy samples by light sheet fluorescence microscopy (LSFM) to quantify extra-cellular matrix proteins