CardioNet: Automatic Semantic Segmentation to Calculate the Cardiothoracic Ratio for Cardiomegaly and Other Chest
Abbas Jafar1, Muhammad Talha Hameed2, Nadeem Akram2
1Department of Computer Engineering, Myongji University, Yongin 03674, Korea.
Insights
CardioNet, an AI model, accurately segments chest X-rays for diagnosing diseases like cardiomegaly. This deep learning approach requires fewer parameters and shows competitive results, aiding early disease detection.
Area of Science:
- Medical Imaging Analysis
- Artificial Intelligence in Healthcare
- Radiology
Background:
- Semantic segmentation of chest X-rays (CXRs) is crucial for diagnosing diseases like cardiomegaly, emphysema, pleural effusions, and pneumothorax.
- Manual analysis of CXRs is time-consuming for medical experts, and automatic segmentation of chest anatomy, particularly the heart and lungs, is challenging due to image quality variations.
- Existing deep learning methods often focus solely on lung segmentation and require extensive training.
Purpose of the Study:
- To develop an efficient deep learning model, CardioNet, for accurate multi-class semantic segmentation of chest anatomy in CXRs.
- To enable early diagnosis of cardiomegaly and other chest-related diseases using AI-driven segmentation.
- To design a model with fewer parameters for improved efficiency compared to conventional deep learning schemes.
Main Methods:
- A novel multiclass concatenation-based automatic semantic segmentation network, CardioNet, was developed.
- CardioNet was designed for fine segmentation of chest anatomy, including the heart, lungs, and clavicle bones.
- The model was evaluated on the JSRT (Japanese Society of Radiological Technology) and Montgomery County (MC) datasets.
Main Results:
- CardioNet achieved acceptable accuracy and competitive performance across multiple datasets for chest anatomy segmentation.
- The model demonstrated effective multi-class segmentation, including heart and lung regions.
- Experimental results indicate the model's capability in diagnosing chest-related diseases through semantic segmentation.
Conclusions:
- CardioNet offers an efficient and accurate solution for semantic segmentation of chest X-rays, facilitating the diagnosis of critical diseases like cardiomegaly.
- The proposed model demonstrates the potential of deep learning with fewer parameters for improved chest imaging analysis.
- CardioNet shows promise for aiding medical practitioners in early disease detection and reducing diagnostic workload.
Abstract:
Semantic segmentation for diagnosing chest-related diseases like cardiomegaly, emphysema, pleural effusions, and pneumothorax is a critical yet understudied tool for identifying the chest anatomy. A dangerous disease among these is cardiomegaly, in which sudden death is a high risk. An expert medical practitioner can diagnose cardiomegaly early using a chest radiograph (CXR). Cardiomegaly is a heart enlargement disease that can be analyzed by calculating the transverse cardiac diameter (TCD) and the cardiothoracic ratio (CTR). However, the manual estimation of CTR and other chest-related diseases requires much time from medical experts. Based on their anatomical semantics, artificial intelligence estimates cardiomegaly and related diseases by segmenting CXRs. Unfortunately, due to poor-quality images and variations in intensity, the automatic segmentation of the lungs and heart with CXRs is challenging. Deep learning-based methods are being used to identify the chest anatomy segmentation, but most of them only consider the lung segmentation, requiring a great deal of training. This work is based on a multiclass concatenation-based automatic semantic segmentation network, CardioNet, that was explicitly designed to perform fine segmentation using fewer parameters than a conventional deep learning scheme. Furthermore, the semantic segmentation of other chest-related diseases is diagnosed using CardioNet. CardioNet is evaluated using the JSRT dataset (Japanese Society of Radiological Technology). The JSRT dataset is publicly available and contains multiclass segmentation of the heart, lungs, and clavicle bones. In addition, our study examined lung segmentation using another publicly available dataset, Montgomery County (MC). The experimental results of the proposed CardioNet model achieved acceptable accuracy and competitive results across all datasets.
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