An Improved Nested U-Net Network for Fluorescence In Situ Hybridization Cell Image Segmentation.

Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely detect and localize nucleic acid sequences. This technique is proving to be an invaluable tool in medical diagnostics and has made significant contributions to biology and the life sciences. However, the number of cells is large and the nucleic acid sequences are disorganized in the FISH images taken using the microscope.

Review of the Research Progress of Human Brain Oxygen Extraction Fraction by Magnetic Resonance Imaging.

In recent years, the incidence of cerebrovascular diseases (CVD) is increasing, which seriously endangers human health. The study on hemodynamics of cerebrovascular disease can help us to understand, prevent, and treat the disease. As one of the important parameters of human cerebral hemodynamics and tissue metabolism, OEF (oxygen extraction fraction) is of great value in central nervous system diseases.

Research on BOLD-fMRI Data Denoising Based on Bayesian Estimation and Adaptive Wavelet Threshold.

The acquisition of functional magnetic resonance imaging (fMRI) images of blood oxygen level-dependent (BOLD) effect and the signals to be analyzed is based on weak changes in the magnetic field caused by small changes in blood oxygen physiological levels, which are weak signals and complex in noise. In order to model and analyze the pathological and hemodynamic parameters of BOLD-fMRI images effectively, it is urgent to use effective signal analysis techniques to reduce the interference of noise and artifacts. In this paper, the noise characteristics of functional magnetic resonance imaging and the traditional signal denoising methods are analyzed.

Diagnosis of left ventricular hypertrophy using convolutional neural network.

Background: Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor's selection point is subjective, and difficult to accurately locate the edge, which will bring errors to the measurement results.

Methods: In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation.