Highlighted Research
For an updated list of publications, please visit my Google Scholar page.
Interpretation of Radiological Imaging Features using Generative Adversarial Networks
We are currently developing an algorithm that generates synthetic images representing the meaning of CNN features, allowing for improved interpretation and potential biomarker exploration. Images below are completely fake!
Fast fully-automated COPD CT lung measurements using deep-learning based lobar segmentation and deformable registration
Diseases affecting the small airways can manifest as pulmonary air trapping, which can go undetected on routine inspiratory chest CT. In many cases, quantitative measurements on a dedicated inspiratory/expiratory lung CT protocol are necessary for air trapping assessment. Recent methods quantify air trapping by registering inspiratory and expiratory phase images using lung deformable registration, but these algorithms often require minutes to hours to perform. We propose a CNN-based algorithm to perform deformable lung registration, reducing inference runtime from as much as ~15 minutes to ~2.25 seconds on CPU and ~1 second on GPU, without loss of accuracy.
Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network
Proposal of a staging system for chronic obstructive pulmonary disease severity. The benefits of this study are multifold. We developed an algorithm that automatically determines the amount of emphysema and air trapping in the lungs on CT images using deep learning-based lung segmentation and deformable registration. We then defined a CT-based staging system to determine COPD severity and showed the proposed staging system is prognosticative of disease progression. Finally, we created disease maps visualizing the spatial distribution of disease. Podcast interview for the paper coming soon...
CNN color-coded difference maps accurately display longitudinal changes in liver MRI-PDFF
Assessment of longitudinal, spatial changes in liver fat requires the use of manually place regions-of-interest on MRI proton density fat fraction images, which is time consuming and laborious. We applied our CNN-based liver registration to create PDFF difference maps to facilitate fast, visual assessment of liver fat. In a reader study, we found visual assessment using our difference maps strongly agreed with manual estimates performed by expert readers.
Comparison of handcrafted features and convolutional neural networks for liver MR image adequacy assessment
Are neural networks always better than traditional machine learning algorithms? We attempt to explore this question through the task of contrast uptake adequacy assessment of liver hepatobiliary images.
Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time
Hepatobiliary phase (HBP) imaging using intracellular contrast facilitates the detection of liver lesions by radiologists, since lesions, along with the liver vessels, appear dark relative to the background liver parenchyma. However, time of the acquisition can range anywhere from 10 to 60 minutes after contrast injection, depending on liver function, number of liver cells (hepatocytes), among other factors. If the image is acquired early, potentially malignant lesions may not be visible; if the image is acquired late, the patient must remain inside the scanner longer than necessary, which can introduce patient discomfort and additional costs to the institution. We developed a deep learning system that automatically determines the adequacy of a liver MR image for lesion detection, which can, in theory, be integrated into the scanner software to select the first adequate HBP acquisition.
Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images
Neural network-based affine liver registration. In clinical practice, radiologists typically manually register a pair of images (e.g., baseline and follow-up) to determine changes in the liver and focal liver lesions. However, due to patient positioning in the scanner, body habitus, and physiological motion, images can appear quite different, even when manually registered. The proposed algorithm is a fast and automated alternative, allowing for better colocalization of the liver and its anatomical structures.
Estimating Mountain Glacier Flowlines by Local Linear Regression Gradient Descent
Determining the flowline of a glacier by applying gradient descent to its corresponding elevation map. Combined with other methods for determining glacier termini, this approach facilitates the large-scale monitoring of mountain glaciers using satellite imaging.
