Fall 2021

CIG is looking for motivated WashU students for the following projects. We have a history of mentoring undergraduate and graduate students interested in research, with many of our alumni becoming PhD students in top programs (including WashU) and many projects leading to publications in top venues (see below). If you are interested in a given project, please apply by emailing your resume and a short description of your interests to the project supervisor.

Regularization by artifact removal for PET

Status: available
Description: Regularization by artifact removal (RARE) is a recently developed technique at CIG that uses deep neural networks for medical image reconstruction. In this project, the student will extend RARE to positron emission tomography (PET). This project will be done in collaboration with the WashU School of Medicine.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov
Areas: Medical Imaging, Deep Learning

Deep learning for diagnosis of post-traumatic elbow disease

Status: available
Description: Deep learning may revolutionize the study of musculoskeletal conditions by automating the evaluation pipeline. This project seeks to develop tissue and modality-specific DL algorithms for automatic evaluation of disease stage and explore multi-modal in post-traumatic elbow conditions using histology, computed tomography, and magnetic resonance imaging. This project is in collaboration with the Musculoskeletal Soft Tissue Laboratory.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Michael David, Spencer Lake, Ulugbek Kamilov
Areas: Image Analysis, Deep Learning

Implementing optimization algorithms in Jupyter Notebook

Status: available
Description: This project is suitable for undergraduate or master’s students who have taken ESE 415 Optimization and have experience with Python programming. The goal is to implement various optimization algorithms using Jupyter Notebook. This is a perfect opportunity to expand one’s knowledge of optimization.
Supervision: Ulugbek Kamilov (kamilov@wustl.edu)
Areas: Large-Scale Optimization, Python

Coordinate-based internal learning for single molecule orientation imaging

Status: available
Description: Single-molecule orientation imaging probes the architecture and organization of biological structures with nanoscale resolution. This collaborative project seeks to extend recently-developed coordinate-based internal learning (CoIL) method to single-molecule orientation imaging.
Supervision: Ulugbek Kamilov (kamilov@wustl.edu), Matthew Lew.
Areas: Large-Scale Optimization, Python

Deep learning for abdominal visceral and subcutaneous fat measurement

Status: reserved (Tee Li)
Description: This project seeks to use MRI to measure: (1) subcutaneous fat (yellow outline), located outside of the abdominal cavity, and (2) visceral fat (red outline), found in the abdominal cavity surrounding vital organs. CIG is seeking a motivated student interested in deep learning for automatic segmentation of abdominal fat. This project will be done in collaboration with the WashU School of Medicine.
Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Paul Commean, Ulugbek Kamilov
Areas: Image Analysis, Medical Imaging

Deep learning for calf muscle and fat measurement

Status: reserved (Yixuan Luo)
Description: This project seeks to use DIXON MRI images to measure subcutaneous fat, bone, muscle, inter-/intramuscular fat, and individual muscle compartments. WashU School of Medicine conducts studies where overweight individuals need their calf muscle automatically measured. Muscle atrophy is a typical consequence of weight loss which is associated with health risks in the older population. CIG is seeking a motivated student interested in using deep learning methods to automatically segment the calf muscle/fat.
Supervision: Xiaojian Xu (xiaojianxu@wustl.edu), Paul Commean, Ulugbek Kamilov
Areas: Image Analysis, Deep Learning

Synthesizing high-resolution videos using deep learning

Status: reserved (Wentao Shangguan)
Description: Video reconstruction is aimed to restore clear videos from corrupted observations, where state-of-the-art methods have achieved promising performance via supervised deep learning technologies. However, those methods require ground-truth videos, which may be inaccessible in some cases. Our goal is to develop a novel video reconstruction algorithm without the need of ground-truth.
Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov
Areas: Deep Learning, Low-Level and Physics-Based Vision

Asynchronous plug-and-play priors with theoretical guarantees

Status: reserved (Hao Qin)
Description: Recent work has illustrated the potential of online plug-and-play priors (PnP) methods for analyzing large amounts of data. This project seeks to theoretically analyze such algorithms in the asynchronous parallel setting. This project is well suited for students interested in expanding their knowledge of optimization.
Supervision: Yu Sun (sun.yu@wustl.edu), Ulugbek Kamilov
Areas: Large-Scale Optimization, Inverse Problems

Learning Optimal Sampling Strategies for MRI

Status: reserved (Dian Jin)
Description: Magnetic resonance imaging can be accelerated by collecting less data in the k-space. This project seeks to develop learning strategies for selecting k-space samples that lead to the best imaging performance. CIG seeks a motivated student interested in deep learning and medical imaging for this project.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Ulugbek Kamilov
Areas: Medical Imaging, Deep Learning

Image Transformers for Computational Imaging

Status: reserved (Zichen Zhang )
Description: Transformers are widely-used architecture for processing sequences by using a self-attention mechanism. This project seeks to investigate the potential of transformers in the context of computational imaging. The focus is on comparing transformers with traditional CNNs on various image reconstruction tasks.
Supervision: Jiaming Liu (jiaming.liu@wustl.edu), Ulugbek Kamilov
Areas: Deep Learning, Image Restoration

Detecting multiple myeloma using deep image analysis

Status: reserved (Flora Sun)
Description: Multiple myeloma (MM) is a devastating form of cancer. The development of imaging tools for MM is an active area with the potential to enable breakthroughs in its diagnosis and treatment. CIG is looking for a motivated student to develop new deep learning methods in the context of our MM collaboration with the researchers at the School of Medicine.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Monica Shokeen, Ulugbek Kamilov
Areas: Image Analysis, Deep Learning

Inhomogeneity correction in quantitative brain MRI

Status: reserved (Eddie Chandler)
Description: Magnetic field inhomogeneities adversely affect MRI images. The correction of such inhomogeneities can be formulated as an optimization problem that exploits their mathematical model and machine learning (ML). CIG is seeking a motivated student interested in using optimization and ML for medical imaging.
Supervision: Weijie Gan (weijie.gan@wustl.edu), Xiaojian Xu, Ulugbek Kamilov
Areas: Image Analysis, Deep Learning

Spring 2021

  • Next generation robust learning algorithms (R. Liu, Y. Sun, and U. S. Kamilov)
  • Accurate calibration of coil sensitivities in MRI (Y. Hu, W. Gan, and U. S. Kamilov)
  • Synthesizing high-resolution videos using deep learning (W. Shangguan, Y. Sun, and U. S. Kamilov)
  • Tunable deep learning for computational imaging (A. Zhou, X. Xu, and U. S. Kamilov)
  • Low-complexity learning for large-scale imaging (Z. Wang, J. Liu, and U. S. Kamilov)
  • Deep learning for diagnosis of post-traumatic elbow disease (Y. Yuan, W. Gan, M. David, S. Lake, and U. S. Kamilov)
  • Deep learning for quantitative brain imaging (E. Chandler, X. Xu, and U. S. Kamilov)

Fall 2020

  • Deep Learning for quantitative brain imaging (E. Chandler, X. Xu, Y. Sun, and U. S. Kamilov)
  • Cycle GAN for quantitative MRI (Y. Li, X. Xu, and U. S. Kamilov)
  • Training robust convolutional neural networks (R. Liu, Y. Sun, and U. S. Kamilov)
  • Implementing CNNs with Tensorflow 2.0 (Z. Wang, Y. Sun, and U. S. Kamilov)
  • Autocalibration for regularization by denoising (M. Xie, Y. Sun, and U. S. Kamilov)

Spring 2020

  • Machine learning for 4D MRI (W. Gan, H. An, U. S. Kamilov)
  • Proximal Optimization for Computerized Tomography (R. Schurz, J. Liu, U. S. Kamilov)
  • End-to-End Training of VAE-GAN Priors (G. Meng, J. Liu, U. S. Kamilov)
  • Design of GAN Prios for Iterative Algorithms (A. Bani, J. Liu, U. S. Kamilov)
  • Recovery Experiments for Deep Learning (J. Liu, Y. Sun, U. S. Kamilov)
  • Autocalibration for Regularization by Denoising (M. Xie, Y. Sun, and U. S. Kamilov)
  • Stochastic Algorithms using Deep Priors (Z. Wu, Y. Sun, U. S. Kamilov)
  • Fast Automatic Focal Plane Selection for Brightfield Microscopy using Machine Learning (Y. Song, X. Xu, N. Laird, Z. Pincus, U. S. Kamilov)
  • Nonlocal Learning for Image Restoration (Y. Li, X. Xu, U. S. Kamilov)
  • Motion-artifact Correction in Quantitative MRI (H. Tang, X. Xu, U. S. Kamilov)

Fall 2019

  • Motion artifact reduction in quantitative brain MRI (N. Woythaler, X. Xu, U. S. Kamilov)
  • Large-Scale Optimization for Computational Imaging (Y. Jia, U. S. Kamilov)
  • Generative Adversarial Networks for Image Restoration (R. Suzuki, X. Xu, U. S. Kamilov)
  • Adversarially robust classifiers for image reconstruction (E. Tang, X. Xu, U. S. Kamilov)
  • Stochastic Algorithms using Deep Priors (Z. Wu, Y. Sun, U. S. Kamilov)
  • Asynchronous large-scale optimization for imaging (Y. Sun, Y. Sun, U. S. Kamilov)
  • Next generation Cryo-EM imaging (M. Xie, Y. Sun, U. S. Kamilov)
  • Computational methods for quantitative brain MR imaging (M. Torop, Y. Sun, U. S. Kamilov)

Spring 2019

  • Deep Single-Molecule Super-Resolution Imaging (S. Xu, Y. Sun, H. Mazidi, M. Lew, U. S. Kamilov)
  • Learning for material imaging with light (Z. Wu, Y. Sun, U. S. Kamilov)
  • Regularization with ConvNets (J. Liu, Y. Sun, U. S. Kamilov)

Fall 2018

  • Regularization with ConvNets (J. Liu, Y. Sun, U. S. Kamilov)
  • Learning for 3D computational imaging (W. Gan, X. Xu, U. S. Kamilov)
  • Convolutional dictionary learning for image reconstruction (F. Long, X. Xu, U. S. Kamilov)
  • Unsupervised deep image learning (H. Zou, Y. Sun, U. S. Kamilov)
  • Distributed algorithms for nonconvex stochastic optimization (M. Abijaoude, U. S. Kamilov)
  • Image restoration with GANs (F. Zhai, Y. Sun, U. S. Kamilov)
  • Learning for MRI (J. Xing, X. Xu, U. S. Kamilov)
  • Learning for material imaging with light (Z. Wu, Y. Sun, U. S. Kamilov)
  • Dictionary Learning for Fourier Ptychographic Microscopy (S. Xu, X. Xu, U. S. Kamilov)