Research | AI-Driven Medical Imaging & Diagnostics Lab

Medical imaging is the technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues (physiology). Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities.

Medical Imaging Modalities

Radiography

Two forms of radiographic images are in use in medical imaging. Projection radiography and fluoroscopy, with the latter being useful for catheter guidance. These 2D techniques are still in wide use despite the advance of 3D tomography due to the low cost, high resolution, and depending on the application, lower radiation dosages with 2D technique. This imaging modality uses a wide beam of X-rays for image acquisition and is the first imaging technique available in modern medicine.

Fluoroscopy produces real-time images of internal structures of the body in a similar fashion to radiography, but employs a constant input of X-rays, at a lower dose rate. Contrast media, such as barium, iodine, and air are used to visualize internal organs as they work. Fluoroscopy is also used in image-guided procedures when constant feedback during a procedure is required.

Projectional radiographs, more commonly known as X-rays, are often used to determine the type and extent of a fracture as well as for detecting pathological changes in the lungs. With the use of radio-opaque contrast media, such as barium, they can also be used to visualize the structure of the stomach and intestines – this can help diagnose ulcers or certain types of colon cancer.

CT Scan Multi-View Visualization
Comprehensive multi-planar reconstruction of neck anatomy for diagnostic assessment.

Magnetic Resonance Imaging

A magnetic resonance imaging instrument (MRI scanner), or "nuclear magnetic resonance (NMR) imaging" scanner as it was originally known, uses powerful magnets to polarize and excite hydrogen nuclei (i.e., single protons) of water molecules in human tissue, producing a detectable signal which is spatially encoded, resulting in images of the body. The MRI machine emits a radio frequency (RF) pulse at the resonant frequency of the hydrogen atoms on water molecules.

Like CT, MRI traditionally creates a two-dimensional image of a thin "slice" of the body and is therefore considered a tomographic imaging technique. Modern MRI instruments are capable of producing images in the form of 3D blocks. Unlike CT, MRI does not involve the use of ionizing radiation and is therefore not associated with the same health hazards.

Brain segmentation with colored overlays

Brain MRI Multi-Structure Segmentation
Deep-learning–based extraction of anatomical brain regions for neuroimaging analysis.

Brain Tumor Segmentation (BraTS Dataset)
Voxel-level tumor delineation from MRI scans for glioma detection and therapeutic planning.

A number of different pulse sequences can be used for specific MRI diagnostic imaging (multiparametric MRI or mpMRI). It is possible to differentiate tissue characteristics by combining two or more of the following imaging sequences: T1-weighted (T1-MRI), T2-weighted (T2-MRI), diffusion weighted imaging (DWI-MRI), dynamic contrast enhancement (DCE-MRI), and spectroscopy (MRI-S). The number of applications of mpMRI for detecting disease in various organs continues to expand, including liver studies, breast tumors, pancreatic tumors, and assessing the effects of vascular disruption agents on cancer tumors.

Ultrasound

Medical ultrasound uses high frequency broadband sound waves in the megahertz range that are reflected by tissue to varying degrees to produce (up to 3D) images. This is commonly associated with imaging the fetus in pregnant women. Uses of ultrasound are much broader, however. Other important uses include imaging the abdominal organs, heart, breast, muscles, tendons, arteries and veins.

3D Pelvic Floor Reconstruction from Ultrasound
Automated segmentation and visualization of pelvic floor musculature for functional assessment.

Volumetric Ultrasound Visualization
High-resolution 3D rendering of anatomical structures using advanced ultrasound processing pipelines.

While it may provide less anatomical detail than techniques such as CT or MRI, it has several advantages which make it ideal in numerous situations, in particular that it studies the function of moving structures in real-time, emits no ionizing radiation, and contains speckle that can be used in elastography. It is very safe to use and does not appear to cause any adverse effects. It is also relatively inexpensive and quick to perform. Ultrasound scanners can be taken to critically ill patients in intensive care units, avoiding the danger caused while moving the patient to the radiology department. The real-time moving image obtained can be used to guide drainage and biopsy procedures. Doppler capabilities on modern scanners allow the blood flow in arteries and veins to be assessed.

Our Lab's Research Focus

Our lab leverages medical imaging in combination with advanced AI techniques to solve challenging problems in healthcare and veterinary medicine. We focus on bridging the gap between raw imaging data and clinically meaningful insights through the following areas:

Organ and Region Segmentation: We develop deep learning algorithms to identify and delineate organs and regions of interest in ultrasound, CT, and MRI scans. Semi-supervised and weakly supervised methods allow high-quality segmentation even with limited labeled data.
Disease Detection and Severity Assessment: Our models integrate imaging with clinical metadata to assess disease presence and severity, providing continuous scoring and supporting early diagnosis. Examples include evaluating fatty liver disease in point-of-care ultrasound and assessing organ structure in neonatal brain scans.
Multimodal Learning and Vision-Language Models: By combining imaging data with textual clinical reports, we create frameworks that generate automated diagnostic captions, interpret radiology reports, and align visual features with clinical language representations for a more complete understanding of disease.
AI-Assisted Clinical Reporting: We develop generative AI models for human and veterinary radiology that produce high-quality, clinically relevant reports. Models are trained iteratively using anonymized clinical datasets and refined with expert radiologist feedback to ensure accuracy, relevance, and consistency.
Sensor-Based Diagnostics: Our lab integrates AI with sensor data to optimize diagnostic devices, such as thermal models for diabetic foot ulcer prevention, combining biomedical engineering, data modeling, and clinical insight.
Translational and Cross-Domain Applications: We adapt AI models across modalities, species, and clinical contexts, creating scalable solutions that work in human medicine, veterinary practice, and research applications. This includes automated evaluation of radiograph quality and species-specific generative models for report generation.

Through these approaches, our lab aims to develop clinically interpretable, robust, and scalable AI solutions that enhance diagnostic efficiency, improve patient outcomes, and extend access to advanced imaging and analysis tools in both human and veterinary healthcare.

Our research draws on a wide range of modalities, AI techniques, and translational applications, demonstrating how medical imaging and computational modeling can be combined to create meaningful real-world impact.