Publications by authors named "Trevor J Chan"
Article Synopsis
- Scientists are studying how tiny structures in the kidneys form during development, especially how they branch out and work together.
- They discovered that as these structures grow, they get packed tightly together, which can change how the cells make decisions.
- The researchers used experiments and math to understand this packing and found that it affects how the kidneys develop over time in mice and might help in creating new tissues for medical purposes.
A Super-Resolution Diffusion Model for Recovering Bone Microstructure from CT Images.
Radiol Artif Intell
November 2023
Purpose: To use a diffusion-based deep learning model to recover bone microstructure from low-resolution images of the proximal femur, a common site of traumatic osteoporotic fractures.
Materials And Methods: Training and testing data in this retrospective study consisted of high-resolution cadaveric micro-CT scans ( = 26), which served as ground truth. The images were downsampled prior to use for model training.
Compression drives diverse transcriptomic and phenotypic adaptations in melanoma.
Proc Natl Acad Sci U S A
September 2023
Physical forces are prominent during tumor progression. However, it is still unclear how they impact and drive the diverse phenotypes found in cancer. Here, we apply an integrative approach to investigate the impact of compression on melanoma cells.
View Article and Find Full Text PDFBackground: Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent of bone mineral density (BMD). Ultrashort echo time (UTE) MRI techniques of measuring cortical bone porosity and geometry have been extensively validated in preclinical studies and have recently been shown to detect impaired bone quality in vivo in patients with osteoporosis. However, these techniques rely on laborious image segmentation, which is clinically impractical.
View Article and Find Full Text PDFMotivation: In this work, we present an analytical method for quantifying both single-cell morphologies and cell network topologies of tumor cell populations and use it to predict 3D cell behavior.
Results: We utilized a supervised deep learning approach to perform instance segmentation on label-free live cell images across a wide range of cell densities. We measured cell shape properties and characterized network topologies for 136 single-cell clones derived from the YUMM1.