Deep learning for contour quality assurance for RTOG 0933: In-silico evaluation.

Purpose: To validate a CT-based deep learning (DL) hippocampal segmentation model trained on a single-institutional dataset and explore its utility for multi-institutional contour quality assurance (QA).

Methods: A DL model was trained to contour hippocampi from a dataset generated by an institutional observer (IO) contouring on brain MRIs from a single-institution cohort. The model was then evaluated on the RTOG 0933 dataset by comparing the treating physician (TP) contours to blinded IO and DL contours using Dice and Haussdorf distance (HD) agreement metrics as well as evaluating differences in dose to hippocampi when TP vs.

Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning.

To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing radiotherapy for lung cancer.. A clinical data set of 58 pre- and post-radiotherapyTc-labeled MAA-SPECT perfusion studies (32 patients) each with contemporaneous 4DCT studies was collected.

Hippocampus segmentation on noncontrast CT using deep learning.

Purpose: Accurate segmentation of the hippocampus for hippocampal avoidance whole-brain radiotherapy currently requires high-resolution magnetic resonance imaging (MRI) in addition to neuroanatomic expertise for manual segmentation. Removing the need for MR images to identify the hippocampus would reduce planning complexity, the need for a treatment planning MR imaging session, potential uncertainties associated with MRI-computed tomography (CT) image registration, and cost. Three-dimensional (3D) deep convolutional network models have the potential to automate hippocampal segmentation.

Analysis of homogeneous layer approximations in the modeling of localized surface plasmon resonance biosensors.

We analyze theoretically the effectiveness of homogeneous layer approximations in modeling localized surface plasmon resonance biosensors made of spherical metal nanoparticles coated with biomolecular layers that have radially variable refractive indices. Using an extended Mie theory, we compute the extinction spectrum and peak wavelength of the system and compare them with when effective medium approximations are applied to treat the biomolecular layer as homogeneous. We investigate how the accuracies of the approximations depend on the geometric parameters of the system and the material of the metal nanoparticle.

LSPR Biosensing Approach for the Detection of Microtubule Nucleation.

Microtubules are dynamic protein filaments that are involved in a number of cellular processes. Here, we report the development of a novel localized surface plasmon resonance (LSPR) biosensing approach for investigating one aspect of microtubule dynamics that is not well understood, namely, nucleation. Using a modified Mie theory with radially variable refractive index, we construct a theoretical model to describe the optical response of gold nanoparticles when microtubules form around them.