Modified characteriograph-assisted testings of spectrozonal analog lab-on-a-chip under laser beams
- “Development of the novel physical methods for complex biomedical diagnostics based on position-sensitive mapping with the angular resolution at the tissue and cellular levels using analytical labs-on-a-chip” (RFBR grant # 16-32-00914) [6 838,27 $ per year; 2016-2017];
- “Lab-on-a-chip development for personalized diagnostics” (FASIE grant 0019125) [3 039,00 $ per year; 2016-2017].
Testings of spectrozonal analog lab-on-a-chip with angle-sensitive pixels (ASP) using diode laser sources in combined nanosecond reflectometric and stroboscopic oscilloscopic measurements.
FILE 1: Combined nanosecond reflectometric and stroboscopic oscilloscopic analysis for RF lab-on-a-chip.mp4
00:15 - Wavelength: 405 nm (DPSS);
00:57 - Wavelength: 650 nm (laser diode source);
FILE 2: Analog angle-sensitive pixel lab-on-a-chip testing using nanosecond stroboscopic oscilloscope.mp4
00:20 - Zero Point Calibration (ZPC);
In this study, adaptive hybrid (AH) scheme is proposed to enhance the measurement accuracy of conventional CD method to acquire the 2-dimensional velocity field of blood flows. It can offer the assistance of the velocity field information measured preliminarily using ultrasound speckle image velocimetry (SIV) technique. Consequently, erroneous vectors in the CD results were replaced with the SIV results. The performance of the proposed AH method was validated by varying flow rate and insonation angle. We compared the AH method with the CD and SIV methods in an agarose vessel model.
A sample of synthetic noise-free reference image created by combining multiple instances of structurally preserved cilia cross-sections. The author has removed the dataset, the interested users can contact the author via email: email@example.com
This dataset is for the paper titled: Segmentation of Cervical Cell Images based on Generative Adversarial Networks. The dataset is used to train and test the Cell-GAN, a generative adversarial network. After training, the Cell-GAN is able to generate a complete single-cell image which has the similar contour to the cell to be segmented.
For the development and evaluation of organ localization methods, we build a set of annotations of organ bounding boxes based on the MICCAI Liver Tumor Segmentation (LiTS) challenge dataset. Bounding boxes of 11 body organs are included: heart (53/28), left lung (52/21), right lung (52/21), liver (131/70), spleen (131/70), pancreas (131/70), left kidney (129/70), right kidney (131/69), bladder (109/67), left femoral head (109/66) and right femoral head (105/66). The number in the parentheses indicates the number of the organs annotated in training and testing sets.
Access the dataset for images of typical diabetic retinopathy lesions and also normal retinal structures annotated at a pixel level, focused on an Indian population. This dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image.
The dataset is divided into three parts:
A. Segmentation: It consists of
1. Original color fundus images (81 images divided into train and test set - JPG Files)
2. Groundtruth images for the Lesions (Microaneurysms, Haemorrhages, Hard Exudates and Soft Exudates divided into train and test set - TIF Files) and Optic Disc (divided into train and test set - TIF Files)
B. Disease Grading: it consists of
1. Original color fundus images (516 images divided into train set (413 images) and test set (103 images) - JPG Files)
2. Groundtruth Labels for Diabetic Retinopathy and Diabetic Macular Edema Severity Grade (Divided into train and test set - CSV File)
C. Localization: It consists of
1. Original color fundus images (516 images divided into train set (413 images) and test set (103 images) -
2. Groundtruth Labels for Optic Disc Center Location (Divided into train and test set - CSV File)
3. Groundtruth Labels for Fovea Center Location (Divided into train and test set - CSV File)
For more information visit idrid.grand-challenge.org