International journal of radiation oncology, biology, physics
31344432
PURPOSE: A noninvasive diagnostic method to predict the degree of malignancy accurately would be of great help in glioma management. This study aimed to create a highly accurate machine learning model to perform glioma grading.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
31313517
We develop a single-mode fiber-based cytometer for the obtaining of two-dimensional (2D) light scattering patterns from static single cells. Anisotropy of the 2D light scattering patterns of single cells from ovarian cancer and normal cell lines is i...
Accurate and reliable segmentation of the prostate gland using magnetic resonance (MR) imaging has critical importance for the diagnosis and treatment of prostate diseases, especially prostate cancer. Although many automated segmentation approaches, ...
Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the white matter of the brain and in the spinal cord. It is thus essential to measure the tissue myelin content to understand the physiopathology of MS, ...
Computer methods and programs in biomedicine
31586789
BACKGROUND AND OBJECTIVES: The retinal fundus contains intricate vascular trees, some of which are mutually intersected and overlapped. The intersection and overlapping of retinal vessels represent vascular junctions (i.e. bifurcation and crossover) ...
Biomechanics and modeling in mechanobiology
31823106
Real-time simulation of organs increases comfort and safety for patients during the surgery. Proper generalized decomposition (PGD) is an efficient numerical method with coordinate errors below 1 mm and response time below 0.1 s that can be used for ...
The present paper proposes a novel kernel adaptive filtering algorithm, where each Gaussian kernel is parameterized by a center vector and a symmetric positive definite (SPD) precision matrix, which is regarded as a generalization of scalar width par...
The purpose of this work is to introduce a novel deep learning strategy to obtain highly accurate dose plan by transforming from a dose distribution calculated using a low-cost algorithm (or algorithmic settings). 25 168 slices of dose distribution a...
We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to ...
PURPOSE: To develop and evaluate machine-learning methods that reconstruct fractional anisotropy (FA) values and mean diffusivities (MD) from 3-direction diffusion MRI (dMRI) acquisitions.