Next Article in Journal
Early Post-Stroke Infections Are Associated with an Impaired Function of Neutrophil Granulocytes
Previous Article in Journal
Emergent Properties of the HNF4α-PPARγ Network May Drive Consequent Phenotypic Plasticity in NAFLD
Previous Article in Special Issue
Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs
Open AccessArticle

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(3), 871; https://doi.org/10.3390/jcm9030871 (registering DOI)
Received: 28 February 2020 / Revised: 17 March 2020 / Accepted: 19 March 2020 / Published: 23 March 2020
Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction. View Full-Text
Keywords: cardiomegaly; cardiothoracic ratio; chest anatomy segmentation; X-Ray-Net cardiomegaly; cardiothoracic ratio; chest anatomy segmentation; X-Ray-Net
Show Figures

Figure 1

MDPI and ACS Style

Arsalan, M.; Owais, M.; Mahmood, T.; Choi, J.; Park, K.R. Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases. J. Clin. Med. 2020, 9, 871.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop