Tag: Segmentation

Deep Learning Empowers Lung MR Imaging for Pulmonary Function Quantification

Deep Learning enables accurate and robust lung tissue classification for assessing pulmonary functional and structural differences between disease cohorts

Dr. Wei Zha, an imaging scientist in the Pulmonary and Metabolic Imaging Center led by Dr. Sean Fain at UW-Madison, has invented a deep learning approach to provide fast, reproducible, and robust quantification for pulmonary structure and function using Oxygen-enhanced (OE) MRI. This novel deep learning technology has great potential to create useful imaging biomarkers for assessing pulmonary diseases and was recently published in the Journal of Magnetic Resonance Imaging. 

Timeline of OE MR Imaging Protocol

OE MRI using a 3D radial ultrashort echo time sequence supports quantitative respiratory function assessment for lung diseases. In contrast to typical lung imaging using hyperpolarized noble gas or fluorinated gas, this novel OE technique does not require specialized multinuclear hardware or expensive specialty gases while providing full chest images of regional ventilation with isotropic spatial resolution. Despite these rapid advances in pulmonary OE MRI, the development of a quantification tool for extracting potential biomarkers and regional image features remain to be developed. The analysis of pulmonary OE MR images remains challenging due to modality-specific complexities, including coil inhomogeneity, arbitrary intensity values, local magnetic susceptibility, and reduced proton density due to the large fraction of air space in the normal lung.

This newly invented deep learning method uses an efficient convolutional encoder-decoder deep learning structure and multi-plane consensus labeling to identify 3D image features and patterns, resulting in an accurate and robust classification and segmentation of pulmonary tissues on OE MR images. Subsequent analysis using the classified tissues yielded robust quantification for lung structure and function and discovered significant differences between different pulmonary disease cohorts.

Deep Learning enables accurate and robust lung tissue classification for assessing pulmonary functional and structural differences between disease cohorts.

SUSAN: Smart AI for Efficient Image Synthesis and Segmentation

In addressing the challenge of creating a generalizable deep learning segmentation technique for magnetic resonance imaging (MRI), the UW-Madison Radiology AI team implemented an approach to seamlessly incorporate highly efficient image-to-image translation using adversarial learning into the segmentation algorithm. This novel technique SUSAN is now published in Magnetic Resonance in Medicine. 

Segmentation is a fundamental step in medical image analysis. While there have been many deep learning methods addressing segmentation challenges in medical images, training highly efficient deep learning models typically requires a large number of training data, which could be extremely expensive and time-consuming. MRI has various image contrasts, making the standard approach of training deep learning models for individual image contrast inefficient and unscalable. Our method, SUSAN, standing for Segmenting Unannotated image Structure using Adversarial Network, was invented to segment different image contrasts using only one set of standard training data. SUSAN adheres to the smart AI philosophy to understand data more efficiently and use information cost-effectively.