Using Limited Memory Lung CT AI to Derive Advanced Quantitative CT Lung Metrics of COPD, ILD, and COVID-19 Pneumonia

Introduction

In this chapter, we look at more advanced AI machine learning computer programs for the assessment of normal and diseased lungs using lung CT AI. These AI programs go beyond the reactive machine methods and use the more advanced limited memory AI methods. We have discussed in detail several reactive machine AI approaches to assessing the presence of emphysema, air trapping, and lung fibrosis in Chapter 5, Chapter 6 . There is a learning component that precedes the use of reactive machine AI algorithms that select the best analytical lung CT AI metric for a given task. This is seen in the LAA ₋₉₅₀ in Chapter 5 and LAA ₋₈₅₆ in Chapter 6 . The learning process is done by trial and error, often using linear regression methods, which are a form of machine learning to decide which analytical lung CT metric is best to assess emphysema on a TLC chest CT scan. The −950 HU threshold was “learned” by looking at several thresholds and determining which threshold corresponds best to other independent measures of emphysema (e.g., lung pathology or pulmonary function testing).

The process of all lung CT AI methods links together a series of four lung CT AI agents that work together to accomplish a final objective to detect and assess diffuse lung disease ( Fig. 7.1 ). The first step is the CT scanner AI program that generates 3D CT images of the entire thorax. The second step is sending these 3D CT images to a lung segmentation AI program that separates the lungs, airways, and pulmonary vessels from the rest of the thoracic anatomy (e.g., heart, aorta, spine, chest wall muscles). This lung segmentation AI agent is a big enabler in making it possible to analyze a large number of chest CT scans with little or no intervention by human beings. The more advanced lung CT AI lung segmentation software programs use limited memory AI approaches including deep learning. For the third step, the lung segmentation AI program passes the images of the lungs to another AI agent that looks for image features in the lung CT images that can be used to predict certain tissue states (e.g., normal, emphysema, pulmonary fibrosis). The lung features that are extracted by reactive machine AI agents have been described in Chapter 5, Chapter 6 . The feature extraction mechanism was hardwired into the reactive memory AI agent without the AI agent needing to learn anything about the lung CT image data. For example, identifying the percent of the lung tissue that was <−950 HU on TLC chest CT scans as a feature of emphysema. The fourth step of lung CT AI is to send the extracted lung CT features to an AI program to detect and assess diffuse lung disease based on the features extracted from the 3D lung CT images. The detection and assessment can be a simple lookup program that assesses if there are any lung CT voxels <−950 HU and, if there are, calculate how many and express this as a percentage of the total lung tissue (e.g., LAA ₋₉₅₀ ) to assess the amount of emphysematous tissue that is present. The LAA ₋₉₅₀ metric has been previously validated as a viable measure of emphysema, as described in Chapter 5 .

In this chapter, we will discuss limited memory (also known as machine learning) lung CT AI agents that train themselves to extract features from the lung CT images that best detect and assess evidence of diseased lung tissue. Supervised training of the limited memory CT AI agent is when the important CT image features are first identified by an expert imaging physician and then the limited memory lung CT agent trains itself to recognize the key features in the image that identify the diseased lung tissue previously identified by the expert imaging physician ( Fig. 7.2 ). The process is unsupervised when the limited memory lung CT AI agent automatically extracts the best imaging features based on the AI agent learning the best lung CT image features to detect and assess the presence of diseased lung tissue ( Fig. 7.3 ).

Supervised training methods of limited memory AI algorithms include support vector machine, decision tree, linear regression, logistic regression, naïve Bayes, k -nearest neighbor, random forest, AdaBoost, and neural network methods. Unsupervised training methods of limited memory AI algorithms include K -means, mean shift, affinity propagation, hierarchical clustering, DBSCAN (density-based spatial clustering of applications with noise), Gaussian mixture modeling, Markov random fields, ISODATA (iterative self-organizing data), and fuzzy C-means systems. Deep machine learning methods such as convolutional neural networks (CNN) are a recent exciting supervised or unsupervised training method that has been applied recently in the detection and assessment of emphysema and COVID-19 pneumonia; more on this later in the chapter.

After the limited memory lung CT AI agent is trained to detect and assess a class of diseased lung tissue, such as emphysema, pulmonary fibrosis, and pneumonia, it is tested on a new set of chest CT cases that have been labeled by an independent method (e.g., by a human who has visually looked at the CT images for evidence of normal lung tissue, emphysema, pulmonary fibrosis). The results of this testing, or validation step, determine the performance of the supervised or unsupervised machine learning algorithms to detect and quantify important features of lung disease. The results of the AI agent in quantitating the amount of important feature(s) in the CT images, such as emphysema, are often correlated with other measures of disease severity or outcomes (e.g., physiology testing and death rate or mortality).

Limited Memory Lung CT AI and the Assessment of Emphysema

Adaptive Multiple Feature Method (AMFM) AI Agent (Supervised, Bayesian Classifier)

The adaptive multiple feature method (AMFM) first described by Uppaluri et al. in 1997 is one of the first lung CT AI papers to use limited memory AI in the assessment of normal and emphysematous lung tissue from chest CT scans. The approach used supervised learning to train the AMFM AI agent. The study had 9 normal subjects and 10 subjects with emphysema. Normal subjects were scanned in the prone position, since they were part of another study looking at interstitial lung disease where prone scanning was done. The emphysematous subjects were scanned in the supine position, since they had advanced COPD and were also being evaluated for lung volume reduction surgery to treat their emphysema. The CT protocol obtained four 3-mm-thick axial images of the lungs obtained using the Imatron Fastrac C-150 XL electron beam CT scanner. Two of the axial CT images were obtained at the level of the carina (tracheal bifurcation), and two were obtained halfway between the carina and the diaphragm.

The different steps that the limited memory AI program AMFM uses are summarized in ( Fig. 7.4 ). The four AMFM steps are in order the following: acquire four 2D lung CT Images, automatically segment the lung tissue from the rest of the thoracic anatomy on the four 2D CT images, expert imaging physician selects regions of interest (ROI) of normal and emphysematous tissue; extract multiple statistical and fractal texture features from training ROI in the four 2D CT images and learn which of these features are optimal for the assessment of normal versus emphysematous lung tissue; detect and assess normal versus emphysema from the test ROI in the four 2D lung CT images based on the prior probability of the extracted features matching normal lung or emphysematous lung.

The third step was accomplished by first having an expert imaging physician label six predefined regions of both the right and left lung on each of the four lung CT images as to whether they were definitely normal or definitely had emphysema. The CT images were then processed so that neighboring voxels of similar values were all assigned an average of the neighboring voxels. These are referred to as the preprocessed ROI. The ROI corresponding to the visually labeled regions of normal and emphysema on the CT images were matched between the unprocessed and processed images. The unprocessed training ROI were then assessed by assessing five first-order statistical features: mean, variance, skewness, kurtosis, grey level entropy, and the geometric fractal dimension. Then the preprocessed training ROI were assessed using eleven second-order statistical features. Five of these second-order statistical features were run-length features and included: short-run emphasis, long-run emphasis, grey level nonuniformity, run-length nonuniformity, and run percentage. The remaining six second-order statistical features were based on the cooccurrence matrix and included: angular second moment, entropy, inertia, contrast, correlation, and inverse difference moment. All of the features were normalized for pixel size and size of the lung in the CT image. The ROI regions that were either definitely normal lung tissue or definitely emphysematous lung tissue were randomly split into two groups of ROI: a training group of ROI used to train the AMFM AI agent and a test set of ROI used to evaluate the ability of the AMFM AI agent to detect normal versus emphysematous ROI. The optimal set of features were selected from the training ROI using the divergence measure along with correlation analysis. Classification into normal and emphysema was done using a Bayesian classifier. The optimal features from the training ROI were used to determine the Bayesian classifier parameters. The ROI from the test set were classified by the Bayesian classifier parameters as to whether the test ROI was normal lung tissue or emphysematous lung tissue. This process could then be repeated to improve the performance/learning of the AMFM lung CT AI agent by adding more labeled normal and emphysematous training and testing ROI. Additional statistical features could also be included to try and improve the performance/learning of the AMFM lung CT AI agent.

The optimal set of features obtained by the AMFM AI agent from the training ROI to separate the normal from emphysematous lung tissue were mean lung density and two run-length features: short-run emphasis and grey level nonuniformity. It is important to note that the mean lung density value of the voxels in the image was an important feature identified by the AMFM AI agent in the training process. Mean lung density is a simple and easy concept to understand that was discussed in Chapter 5 . The fundamental lung tissue parameter measured by the CT scans of the lung is lung density. Lung density is known to decrease in patients with emphysema.

The AMFM AI agent was compared to the MLD and the 5th percentile histogram methods for identifying normal versus emphysematous lung. The histogram method looked at the CT number in HU threshold where the lowest 5% of lung voxels occurred. In distinguishing normal lung from emphysematous lung, the AMFM method was 100% accurate, 5th percentile histogram method was 97.4% accurate, and the MLD method was 94.7% accurate. The AMFM AI agent achieved a modest improvement in identifying normal versus emphysematous lung tissue compared to the simpler reactive machine lung CT AI methods in this study.