Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.

Volumes of coronary plaque disease in relation to body mass index, waist circumference, truncal fat mass and epicardial adipose tissue in patients with type 2 diabetes mellitus and controls

OBJECTIVES

Coronary atherosclerosis in patients with type 2 diabetes mellitus may be promoted by regional fat distribution. We investigated the association between anthropometric measures of obesity, truncal fat mass, epicardial adipose tissue and coronary atherosclerosis in asymptomatic patients and matched controls.

METHODS

We examined 44 patients and 59 controls [mean (standard deviation) age 64.4 ± 9.9 vs 61.8 ± 9.7, male 50% vs 47%, diabetes duration mean (standard deviation) 7.7 ± 1.5] with coronary computed tomography angiography. Coronary plaques were quantified as total, calcified, non-calcified and low-density non-calcified plaque volumes (mm³). Regional fat distribution was assessed by dual-energy X-ray absorptiometry, body mass index (kg/m²), waist circumference (cm) and epicardial fat volume (mm³). Endothelial function and systemic inflammation were evaluated by peripheral arterial tonometry (log transformed Reactive Hyperemia Index) and C-reactive protein (mg/L).

RESULTS

Body mass index and waist circumference (p < 0.02) were associated with coronary plaque volumes. Body mass index was associated with low-density non-calcified plaque volume after adjustment for age, sex and diabetes status (p < 0.01). Truncal fat mass (p > 0.51), waist circumference (p > 0.06) and epicardial adipose tissue (p > 0.17) were not associated with coronary plaque volumes in adjusted analyses.

CONCLUSION

Body mass index is associated with coronary plaque volumes in diabetic as well as non-diabetic individuals.

SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images

This study aims to automatically diagnose thoracic diseases depicted on the chest x-ray (CXR) images using deep convolutional neural networks. The existing methods generally used the entire CXR images for training purposes, but this strategy may suffer from two drawbacks. First, potential misalignment or the existence of irrelevant objects in the entire CXR images may cause unnecessary noise and thus limit the network performance. Second, the relatively low image resolution caused by the resizing operation, which is a common pre-processing procedure for training neural networks, may lead to the loss of image details, making it difficult to detect pathologies with small lesion regions. To address these issues, we present a novel method termed as segmentation-based deep fusion network (SDFN), which leverages the domain knowledge and the higher-resolution information of local lung regions. Specifically, the local lung regions were identified and cropped by the Lung Region Generator (LRG). Two CNN-based classification models were then used as feature extractors to obtain the discriminative features of the entire CXR images and the cropped lung region images. Lastly, the obtained features were fused by the feature fusion module for disease classification. Evaluated by the NIH benchmark split on the Chest X-ray 14 Dataset, our experimental result demonstrated that the developed method achieved more accurate disease classification compared with the available approaches via the receiver operating characteristic (ROC) analyses. It was also found that the SDFN could localize the lesion regions more precisely as compared to the traditional method.

Artificial Intelligence in Cardiovascular Medicine

PURPOSE OF REVIEW

The ripples of artificial intelligence are being felt in various sectors of human life. Machine learning, a subset of artificial intelligence, extracts information from large databases of information and is gaining traction in various fields of cardiology. In this review, we highlight noteworthy examples of machine learning utilization in echocardiography, nuclear cardiology, computed tomography, and magnetic resonance imaging over the past year.

RECENT FINDINGS

In the past year, machine learning (ML) has expanded its boundaries in cardiology with several positive results. Some studies have integrated clinical and imaging information to further augment the accuracy of these ML algorithms. All the studies mentioned in this review have clearly demonstrated superior results of ML in relation to conventional approaches for identifying obstructions or predicting major adverse events in reference to conventional approaches. As the influx of data arriving from gradually evolving technologies in health care and wearable devices continues to be more complex, ML may serve as the bridge to transcend the gap between health care and patients in the future. In order to facilitate a seamless transition between both, a few issues must be resolved for a successful implementation of ML in health care.

State-of-the-art review article. Atherosclerosis affecting fat: What can we learn by imaging perivascular adipose tissue?

Perivascular adipose tissue (PVAT) surrounding the human coronary arteries, secretes a wide range of adipocytokines affecting the biology of the adjacent vascular wall in a paracrine way. However, we have recently found that PVAT also behaves as a sensor of signals coming from the vascular wall, to which it reacts by changing its morphology and secretory profile. Indeed, vascular inflammation, a key feature of vascular disease pathogenesis, leads to the release of inflammatory signals that disseminate into local fat, inducing local lipolysis and inhibiting adipogenesis. This ability of PVAT to sense inflammatory signals from the vascular wall, can be used as a "thermometer" of the vascular wall, allowing for non-invasive detection of coronary inflammation. Vascular inflammation induces a shift of PVAT's composition from lipid to aqueous phase, resulting into increased computed tomography (CT) attenuation around the inflamed artery, forming a gradient with increasing attenuation closer to the inflamed coronary artery wall. These spatial changes in PVAT's attenuation are easily detected around culprit lesions during acute coronary syndromes. A new biomarker designed to captured these spatial changes in PVAT's attenuation around the human coronary arteries, the Fat Attenuation Index (FAI), has additional predictive value in stable patients for cardiac mortality and non-fatal heart attacks, above the prediction provided by the current state of the art that includes risk factors, calcium score and presence of high risk plaque features. The use of perivascular FAI in clinical practice may change the way we interpret cardiovascular CT angiography, as it is applicable to any coronary CT angiogram, and it offers dynamic information about the inflammatory burden of the coronary arteries, providing potential guidance for preventive measures and invasive treatments.

Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review

Data science is likely to lead to major changes in cardiovascular imaging. Problems with timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The application of artificial intelligence (AI) is dependent on robust data; the application of appropriate computational approaches and tools; and validation of its clinical application to image segmentation, automated measurements, and eventually, automated diagnosis. AI may reduce cost and improve value at the stages of image acquisition, interpretation, and decision-making. Moreover, the precision now possible with cardiovascular imaging, combined with "big data" from the electronic health record and pathology, is likely to better characterize disease and personalize therapy. This review summarizes recent promising applications of AI in cardiology and cardiac imaging, which potentially add value to patient care.

Deep learning-based stenosis quantification from coronary CT Angiography

BACKGROUND

Coronary computed tomography angiography (CTA) allows quantification of stenosis. However, such quantitative analysis is not part of clinical routine. We evaluated the feasibility of utilizing deep learning for quantifying coronary artery disease from CTA.

METHODS

A total of 716 diseased segments in 156 patients (66 ± 10 years) who underwent CTA were analyzed. Minimal luminal area (MLA), percent diameter stenosis (DS), and percent contrast density difference (CDD) were measured using semi-automated software (Autoplaque) by an expert reader. Using the expert annotations, deep learning was performed with convolutional neural networks using 10-fold cross-validation to segment CTA lumen and calcified plaque. MLA, DS and CDD computed using deep-learning-based approach was compared to expert reader measurements.

RESULTS

There was excellent correlation between the expert reader and deep learning for all quantitative measures (r=0.984 for MLA; r=0.957 for DS; and r=0.975 for CDD, p<0.001 for all). The expert reader and deep learning method was not significantly different for MLA (median 4.3 mm2 for both, p=0.68) and CDD (11.6 vs 11.1%, p=0.30), and was significantly different for DS (26.0 vs 26.6%, p<0.05); however, the ranges of all the quantitative measures were within inter-observer variability between 2 expert readers.

CONCLUSIONS

Our deep learning-based method allows quantitative measurement of coronary artery disease segments accurately from CTA and may enhance clinical reporting.

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction, and intervention. Deep learning is a representation learning method that consists of layers that transform data nonlinearly, thus, revealing hierarchical relationships and structures. In this review, we survey deep learning application papers that use structured data, and signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

Cardiac CT: Technological Advances in Hardware, Software, and Machine Learning Applications

Purpose of Review

Multidetector row computed tomography (CT) allows noninvasive imaging of the heart and coronary arteries. The purpose of this review is to briefly summarize recent advances in CT hardware and software technology, and machine learning applications for cardiovascular imaging.

Recent Findings

In the last decades, there have been significant improvements in CT hardware focusing on faster gantry rotation resulting in improved temporal resolution. Concurrent hardware improvements include improved spatial resolution and higher coverage of the patient, enabling faster acquisition. Advances in cardiac CT software include methods for measurement of noninvasive FFR, coronary plaque characterization, and adipose tissue characteristics around the heart. Machine learning approaches using cardiac CT have been shown to improve both risk of prognosis and lesion-specific ischemia.

Summary

Recent advances in CT hardware and software have expanded the clinical utility of CT for cardiovascular imaging. In the next decades, continued advances can be anticipated in these areas, and in machine learning applications in cardiac CT, as they are incorporated into clinical routine for image acquisition, image analysis, and prediction of patient outcomes.