scholarly journals Whole slide imaging system using deep learning-based automated focusing

2019 ◽  
Vol 11 (1) ◽  
pp. 480 ◽  
Author(s):  
Tathagato Rai Dastidar ◽  
Renu Ethirajan
Keyword(s):  
Deep Learning ◽  
Imaging System ◽  
Whole Slide Imaging

scholarly journals Low-cost whole slide imaging system withsingle-shot autofocusing based on colormultiplexed illumination and deep learning

2021 ◽  
Author(s):  
Kaifa Xin ◽  
Shaowei Jiang ◽  
xu chen ◽  
Yonghong He ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Imaging System ◽  
Low Cost ◽  
Whole Slide Imaging

scholarly journals Synthetic polarization-sensitive optical coherence tomography by deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yi Sun ◽  
Jianfeng Wang ◽  
Jindou Shi ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Imaging System ◽  
Imaging Modality ◽  
Similarity Index ◽  
Polarization Sensitivity ◽  
Imaging Method ◽  
Optical Coherence ◽  
The Real ◽  
Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

scholarly journals Transform- and multi-domain deep learning for single-frame rapid autofocusing in whole slide imaging

2018 ◽  
Vol 9 (4) ◽  
pp. 1601 ◽  
Author(s):  
Shaowei Jiang ◽  
Jun Liao ◽  
Zichao Bian ◽  
Kaikai Guo ◽  
Yongbing Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Whole Slide Imaging ◽  
Single Frame

Target recognition method based on single-pixel imaging system and deep learning in the noisy environment

2020 ◽  
Vol 49 (6) ◽  
pp. 20200010
Author(s):  
石峰 Feng Shi ◽  
陆同希 Tongxi Lu ◽  
杨书宁 Shuning Yang ◽  
苗壮 Zhuang Miao ◽  
杨晔 Ye Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Imaging System ◽  
Target Recognition ◽  
Noisy Environment ◽  
Recognition Method ◽  
Single Pixel

scholarly journals Mango Fruit Load Estimation Using a Video Based MangoYOLO—Kalman Filter—Hungarian Algorithm Method

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2742 ◽  
Author(s):  
Wang ◽  
Walsh ◽  
Koirala
Keyword(s):  
Deep Learning ◽  
Kalman Filter ◽  
Imaging System ◽  
Tracking System ◽  
Video Tracking ◽  
Fruit Tree ◽  
Fruit Yield ◽  
Image Method ◽  
Mango Fruit ◽  
Hungarian Algorithm

: Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree (“dual-view”) underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame. The Hungarian algorithm was used to correlate fruit between neighbouring frames, with the improvement of enabling multiple-to-one assignment. The Kalman filter was used to predict the position of fruit in following frames, to avoid multiple counts of a single fruit that is obscured or otherwise not detected with a frame series. A “borrow” concept was added to the Kalman filter to predict fruit position when its precise prediction model was absent, by borrowing the horizontal and vertical speed from neighbouring fruit. By comparison with human count for a video with 110 frames and 192 (human count) fruit, the method produced 9.9% double counts and 7.3% missing count errors, resulting in around 2.6% over count. In another test, a video (of 1162 frames, with 42 images centred on the tree trunk) was acquired of both sides of a row of 21 trees, for which the harvest fruit count was 3286 (i.e., average of 156 fruit/tree). The trees had thick canopies, such that the proportion of fruit hidden from view from any given perspective was high. The proposed method recorded 2050 fruit (62% of harvest) with a bias corrected Root Mean Square Error (RMSE) = 18.0 fruit/tree while the dual-view image method (also using MangoYOLO) recorded 1322 fruit (40%) with a bias corrected RMSE = 21.7 fruit/tree. The video tracking system is recommended over the dual-view imaging system for mango orchard fruit count.

scholarly journals A Deep Learning-Based Scatter Correction of Simulated X-ray Images

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 944 ◽  
Author(s):  
Heesin Lee ◽  
Joonwhoan Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Imaging System ◽  
Scatter Correction ◽  
Similarity Index ◽  
Real Data ◽  
Correction Method ◽  
Training Data ◽  
X Ray ◽  
Scatter Reduction

X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively.

scholarly journals Use of a whole-slide imaging system to assess the presence and alteration of lymphatic vessels in joint sections of arthritic mice

2012 ◽  
Vol 88 (8) ◽  
pp. 428-439 ◽  
Author(s):  
JX Shi ◽  
QQ Liang ◽  
YJ Wang ◽  
RA Mooney ◽  
BF Boyce ◽  
...  
Keyword(s):  
Imaging System ◽  
Lymphatic Vessels ◽  
Whole Slide Imaging

scholarly journals InstantScope: a low-cost whole slide imaging system with instant focal plane detection

2015 ◽  
Vol 6 (9) ◽  
pp. 3210 ◽  
Author(s):  
Kaikai Guo ◽  
Jun Liao ◽  
Zichao Bian ◽  
Xin Heng ◽  
Guoan Zheng
Keyword(s):  
Focal Plane ◽  
Imaging System ◽  
Low Cost ◽  
Whole Slide Imaging

scholarly journals Blood Content Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 308-313
Keyword(s):  
Deep Learning ◽  
Blood Cells ◽  
Imaging System ◽  
Circulatory System ◽  
Medical Field ◽  
Naked Eye ◽  
Related Disease ◽  
Blood Disease ◽  
Learning Techniques ◽  
The Given

Cells in the human circulatory system and identifying the types and its functionalities cannot be done through naked eye. This asks for greater accurate methods of visualizing it and hence is vital in understanding blood disease causes, symptoms and the solution for them. But this field lacked clearance for the imaging system. Image Recognition was innovated using Deep Learning Technique.Human body cells assume an astounding job in the human resistant framework. To know more about blood-related infections and its effects, pathologists need to think about the attributes of cells. To diagnose a blood related disease, we need to identify and characterize blood samples of patients. In the medical field, automation for detecting and classifying blood cells and its subtypes have gained more importance nowadays. Recognition of an object is a basic piece for the vision of a computer that distinguishes an article in the given picture regardless of foundations, impediment, lighting or the edge of the view. Problems that are too difficult to solve can be handled using architectures that run deep using algorithms that dive deeper into the features extracted from the input and this can be possible using Deep Learning.

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