Deep learning based fully automatic segmentation of the left ventricular endocardium and epicardium from cardiac cine MRI

Validation of a deep learning-based software for automated analysis of T2 mapping in cardiac magnetic resonance imaging

Background

The reliability and diagnostic performance of deep learning (DL)-based automated T2 measurements on T2 map of 3.0-T cardiac magnetic resonance imaging (MRI) using multi-institutional datasets have not been investigated. We aimed to evaluate the performance of a DL-based software for measuring automated T2 values from 3.0-T cardiac MRI obtained at two centers.

Methods

Eighty-three subjects were retrospectively enrolled from two centers (42 healthy subjects and 41 patients with myocarditis) to validate a commercial DL-based software that was trained to segment the left ventricular myocardium and measure T2 values on T2 mapping sequences. Manual reference T2 values by two experienced radiologists and those calculated by the DL-based software were obtained. The segmentation performance of the DL-based software and the non-inferiority of automated T2 values were assessed compared with the manual reference standard per segment level. The software's performance in detecting elevated T2 values was assessed by calculating the sensitivity, specificity, and accuracy per segment.

Results

The average Dice similarity coefficient for segmentation of myocardium on T2 maps was 0.844. The automated T2 values were non-inferior to the manual reference T2 values on a per-segment analysis (45.35 vs. 44.32 ms). The DL-based software exhibited good performance (sensitivity: 83.6-92.8%; specificity: 82.5-92.0%; accuracy: 82.7-92.2%) in detecting elevated T2 values.

Conclusions

The DL-based software for automated T2 map analysis yields non-inferior measurements at the per-segment level and good performance for detecting myocardial segments with elevated T2 values compared with manual analysis.

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMRI) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians as each scan has many slices of data, and the contrast of it might be low. To address these issues, deep learning (DL) techniques have been employed in the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. The following presents research to detect CVDs using CMR images and the most significant DL methods. Another section discussed the challenges in diagnosing CVDs from CMRI data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. Finally, the most important findings of this study are presented in the conclusion section.

Dark blood T2-weighted imaging of the human heart with AI-assisted compressed sensing: a patient cohort study

Background

Dark blood T2-weighted (DB-T2W) imaging is widely used to evaluate myocardial edema in myocarditis and inflammatory cardiomyopathy. However, this technique is sensitive to arrhythmia, tachycardia, and cardiac and respiratory motion due to the long scan time with multiple breath-holds. The application of artificial intelligence (AI)-assisted compressed sensing (ACS) has facilitated significant progress in accelerating medical imaging. However, the effect of DB-T2W imaging on ACS has not been elucidated. This study aimed to examine the effects of ACS on the image quality of single-shot and multi-shot DB-T2W imaging of edema.

Methods

Thirty-three patients were included in this study and received DB-T2W imaging with ACS, including single-shot acquisition (SS-ACS) and multi-shot acquisition (MS-ACS). The resulting images were compared with those of the conventional multi-shot DB-T2W imaging with parallel imaging (MS-PI). Quantitative assessments of the signal-to-noise ratio (SNR), tissue contrast ratio (CR), and contrast-to-noise ratio (CNR) were performed. Three radiologists independently evaluated the overall image quality, blood nulling, free wall of the left ventricle, free wall of the right ventricle, and interventricular septum using a 5-point Likert scale.

Results

The total scan time of the DB-T2W imaging with ACS was significantly reduced compared to the conventional parallel imaging [number of heartbeats (SS-ACS:MS-ACS:MS-PI) =19:63:99; P<0.001]. The SNR_myocardium and CNR_{blood-myocardium} of MS-ACS and SS-ACS were higher than those of MS-PI (all P values <0.01). Furthermore, the CR_{blood-myocardium} of SS-ACS was also higher than that of MS-PI (P<0.01). There were significant differences in overall image quality, blood nulling, left ventricle free wall visibility, and septum visibility between the MS-PI, MS-ACS, and SS-ACS protocols (all P values <0.05). Moreover, blood in the heart was better nulled using SS-ACS (P<0.01).

Conclusions

The ACS method shortens the scan time of DB-T2W imaging and achieves comparable or even better image quality compared to the PI method. Moreover, DB-T2W imaging using the ACS method can reduce the number of breath-holds to 1 with single-shot acquisition.

Automated knee cartilage segmentation for heterogeneous clinical MRI using generative adversarial networks with transfer learning

BACKGROUND

This study aimed to build a deep learning model to automatically segment heterogeneous clinical MRI scans by optimizing a pre-trained model built from a homogeneous research dataset with transfer learning.

METHODS

Conditional generative adversarial networks pretrained on the Osteoarthritis Initiative MR images was transferred to 30 sets of heterogenous MR images collected from clinical routines. Two trained radiologists manually segmented the 30 sets of clinical MR images for model training, validation and test. The model performance was compared to models trained from scratch with different datasets, as well as two radiologists. A 5-fold cross validation was performed.

RESULTS

The transfer learning model obtained an overall averaged Dice coefficient of 0.819, an averaged 95 percentile Hausdorff distance of 1.463 mm, and an averaged average symmetric surface distance of 0.350 mm on the 5 random holdout test sets. A 5-fold cross validation had a mean Dice coefficient of 0.801, mean 95 percentile Hausdorff distance of 1.746 mm, and mean average symmetric surface distance of 0.364 mm. It outperformed other models and performed similarly as the radiologists.

CONCLUSIONS

A transfer learning model was able to automatically segment knee cartilage, with performance comparable to human, using heterogeneous clinical MR images with a small training data size. In addition, the model proved robust when tested through cross validation and on images from a different vendor. We found it feasible to perform fully automated cartilage segmentation of clinical knee MR images, which would facilitate the clinical application of quantitative MRI techniques and other prediction models for improved patient treatment planning.

MRI-guided cardiac-induced target motion tracking for atrial fibrillation cardiac radioablation

BACKGROUND AND PURPOSE

Atrial fibrillation (AF) cardiac radioablation (CR) challenges radiotherapy tracking: multiple small targets close to organs-at-risk undergo rapid differential cardiac contraction and respiratory motion. MR-guidance offers a real-time target tracking solution. This work develops and investigates MRI-guided tracking of AF CR targets with cardiac-induced motion.

MATERIALS AND METHODS

A direct tracking method (Tracking_direct) and two indirect tracking methods leveraging population-based surrogacy relationships with the left atria (Tracking_{indirect_LA}) or other target (Tracking_{indirect_target}) were developed. Tracking performance was evaluated using transverse ECG-gated breathhold MRI images from 15 healthy and 10 AF participants. Geometric and volumetric tracking errors were calculated, defined as the difference between the ground-truth and tracked target centroids and volumes respectively. Transverse, breath-hold, noncardiac-gated cine images were acquired at 4 Hz in 5 healthy and 5 AF participants to qualitatively characterize tracking performance on images more comparable to MRILinac acquisitions.

RESULTS

The average 3D geometric tracking errors for Tracking_direct, Tracking_{indirect_LA} and Tracking_{indirect_target} respectively were 1.7 ± 1.2 mm, 1.6 ± 1.1 mm and 1.9 ± 1.3 mm in healthy participants and 1.7 ± 1.3 mm, 1.5 ± 1.0 mm and 1.7 ± 1.2 mm in AF participants. For Tracking_direct, 88% of analyzed images had 3D geometric tracking errors <3 mm and the average volume tracking error was 1.7 ± 1.3 cc. For Tracking_direct on non-cardiac-gated cine images, tracked targets overlapped organsat-risk or completely missed the target area on 2.2% and 0.08% of the images respectively.

CONCLUSION

The feasibility of non-invasive MRI-guided tracking of cardiac-induced AF CR target motion was demonstrated for the first time, showing potential for improving AF CR treatment efficacy.

An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline

Parameterised patient-specific models of the heart enable quantitative analysis of cardiac function as well as estimation of regional stress and intrinsic tissue stiffness. However, the development of personalised models and subsequent simulations have often required lengthy manual setup, from image labelling through to generating the finite element model and assigning boundary conditions. Recently, rapid patient-specific finite element modelling has been made possible through the use of machine learning techniques. In this paper, utilising multiple neural networks for image labelling and detection of valve landmarks, together with streamlined data integration, a pipeline for generating patient-specific biventricular models is applied to clinically-acquired data from a diverse cohort of individuals, including hypertrophic and dilated cardiomyopathy patients and healthy volunteers. Valve motion from tracked landmarks as well as cavity volumes measured from labelled images are used to drive realistic motion and estimate passive tissue stiffness values. The neural networks are shown to accurately label cardiac regions and features for these diverse morphologies. Furthermore, differences in global intrinsic parameters, such as tissue anisotropy and normalised active tension, between groups illustrate respective underlying changes in tissue composition and/or structure as a result of pathology. This study shows the successful application of a generic pipeline for biventricular modelling, incorporating artificial intelligence solutions, within a diverse cohort.

Discrete light sheet microscopic segmentation of left ventricle using morphological tuning and active contours

Left ventricular segmentation using cardiovascular MR scan is required for the diagnosis and further cure of cardiac diseases. Automatic systems for left ventricle segmentation are being studied for attaining more accurate results in a shorter period of time. A novel algorithm introducing discrete segmentation of left ventricle achieves an independent processing of images swiftly. The workflow consists of four segments; first, automated localization is performed on the MR image. Second, performing preprocessing intimately improves and enhances the quality of image using mean contrast adjustment. Central segmentation of endocardium and epicardium layers includes novel MTAC (Morphological tuning using active contours) segmentation algorithm that provides a perfect combination of active contours and morphological tuning to bring an adequate and desirable segmentation. The prospective snake model is a restrained progression, which takes iterations for an impulse throughout the left ventricle contours. At the end, contrast based refining overcomes minor edge problems for both outer and inner boundaries. Proposed algorithm is evaluated via Sunnybrook cardiac MR images by producing an overall average perpendicular distance 2.45 mm, an average dice matrix (endo: 91.3%; epi: 92.16%) and 91.7% dice matrix of overall endocardium and epicardium contours from ground truth contours.

Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging

Deep learning algorithms for left ventricle (LV) segmentation are prone to bias towards the training dataset. This study assesses sex- and age-dependent performance differences when using deep learning for automatic LV segmentation. Retrospective analysis of 100 healthy subjects undergoing cardiac MRI from 2012 to 2018, with 10 men and women in the following age groups: 18-30, 31-40, 41-50, 51-60, and 61-80 years old. Subjects underwent 1.5 T, 2D CINE SSFP MRI. 35 pathologic cases from local clinical exams and the SCMR 2015 consensus contours dataset were also analyzed. A fully convolutional network (FCN) similar to U-Net trained on the U.K. Biobank was used to automatically segment LV endocardial and epicardial contours. FCN and manual segmentation were compared using Dice metrics and measurements of end-diastolic volume (EDV), end-systolic volume (ESV), mass (LVM), and ejection fraction (LVEF). Paired t-tests and linear regressions were used to analyze measurement differences with respect to sex and age. Dice metrics (median ± IQR) for n = 135 cases were 0.94 ± 0.04/0.87 ± 0.10 (ED endocardium/ES endocardium). Measurement biases (mean ± SD) among the healthy cohort were - 0.3 ± 10.1 mL for EDV, - 6.7 ± 9.6 mL for ESV, 4.6 ± 6.4% for LVEF, and - 2.2 ± 11.0 g for LVM; biases were independent of sex and age. Biases among the 35 pathologic cases were 0.1 ± 19 mL for EDV, - 4.8 ± 19 mL for ESV, 2.0 ± 7.6% for LVEF, and 1.0 ± 20 g for LVM. In conclusion, automatic segmentation by the Biobank-trained FCN was independent of age and sex. Improvements in end-systolic basal slice detection are needed to decrease bias and improve precision in ESV and LVEF.