Deep learning for classification of late gadolinium enhancement lesions based on the 16-segment left ventricular model.

PURPOSE: This study aimed to develop and validate a deep learning-based method that allows for segmental analysis of myocardial late gadolinium enhancement (LGE) lesions. METHODS: Cardiac LGE data from 170 patients with coronary artery disease and non-ischemic heart disease were used for training, validation, and testing. Short-axis images were transformed to polar space after identification of the left ventricular (LV) center point and anterior right ventricular (RV) insertion point. Images were obtained after dividing the polar transformed images into segments based on the 16-segment LV model. Five different deep convolutional neural network (CNN) models were developed and validated using the labeled data, where the image after the division corresponded to a segment, and the lesion labeling was based on the 16-segment LV model. Unseen testing data were used to evaluate the performance of the lesion classification. RESULTS: Without manual lesion segmentation and annotation, the proposed method showed an area under the curve (AUC) of 0.875, and a precision, recall, and F1-score of 0.723, 0.783, and 0.752, respectively for the lesion class when the pretrained ResNet50 model was tested for all slice images. The two pretrained models of ResNet50 and EfficientNet-B0 outperformed the three non-pretrained CNN models in terms of AUCs (0.873-0.875 vs. 0.834-0.841). CONCLUSION: The proposed method is based on learning a deep CNN model from polar transformed images to predict LGE lesions with good accuracy and does not require time-consuming annotation procedures such as lesion segmentation.

Utilizing Synthetic Nodules for Improving Nodule Detection in Chest Radiographs.

Algorithms that automatically identify nodular patterns in chest X-ray (CXR) images could benefit radiologists by reducing reading time and improving accuracy. A promising approach is to use deep learning, where a deep neural network (DNN) is trained to classify and localize nodular patterns (including mass) in CXR images. Such algorithms, however, require enough abnormal cases to learn representations of nodular patterns arising in practical clinical settings. Obtaining large amounts of high-quality data is impractical in medical imaging where (1) acquiring labeled images is extremely expensive, (2) annotations are subject to inaccuracies due to the inherent difficulty in interpreting images, and (3) normal cases occur far more frequently than abnormal cases. In this work, we devise a framework to generate realistic nodules and demonstrate how they can be used to train a DNN identify and localize nodular patterns in CXR images. While most previous research applying generative models to medical imaging are limited to generating visually plausible abnormalities and using these patterns for augmentation, we go a step further to show how the training algorithm can be adjusted accordingly to maximally benefit from synthetic abnormal patterns. A high-precision detection model was first developed and tested on internal and external datasets, and the proposed method was shown to enhance the model's recall while retaining the low level of false positives.

EVCMR: A tool for the quantitative evaluation and visualization of cardiac MRI data.

Quantitative evaluation of diseased myocardium in cardiac magnetic resonance imaging (MRI) plays an important role in the diagnosis and prognosis of cardiovascular disease. The development of a user interface with state-of-the-art techniques would be beneficial for the efficient post-processing and analysis of cardiac images. The aim of this study was to develop a custom user interface tool for the quantitative evaluation of the short-axis left ventricle (LV) and myocardium. Modules for cine, perfusion, late gadolinium enhancement (LGE), and T1 mapping data analyses were developed in Python, and a module for three-dimensional (3D) visualization was implemented using PyQtGraph library. The U-net segmentation and manual contour correction in the user interface were effective in generating reference myocardial segmentation masks, which helped obtain labeled data for deep learning model training. The proposed U-net segmentation resulted in a mean Dice score of 0.87 (±0.02) in cine diastolic myocardial segmentation. The LV mass measurement of the proposed method showed good agreement with that of manual segmentation (intraclass correlation coefficient = 0.97, mean difference and 95% Bland-Altman limits of agreement = 4.4 ± 12.2 g). C++ implementation of voxel-wise T1 mapping and its binding via pybind11 led to a significant computational gain in calculating the T1 maps. The 3D visualization enabled fast user interactions in rotating and zooming-in/out of the 3D myocardium and scar transmurality. The custom tool has the potential to provide a fast and comprehensive analysis of the LV and myocardium from multi-parametric MRI data in clinical settings.