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Two-Stage CNN Whole Heart Segmentation Combining Image Enhanced Attention Mechanism and Metric Classification
Xuchu Wang1, Fusheng Wang2, Yanmin Niu3
1Key Laboratory of Optoelectronic Technology and Systems of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing, 400040, China. xcwang@cqu.edu.cn.
Insights
This study introduces a novel two-stage convolutional neural network (CNN) for cardiac MRI segmentation. The method enhances tissue feature extraction and boundary refinement for improved computer-aided cardiovascular diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiovascular Diagnostics
Background:
- Accurate segmentation of cardiac tissues and organs in magnetic resonance imaging (MRI) is crucial for computer-aided cardiovascular diagnosis.
- Challenges include complex tissue distribution, low feature discriminability, and large organ sizes in cardiac MRI slices.
- Existing methods struggle with segmenting subtle and adherent tissue boundaries.
Purpose of the Study:
- To develop an advanced segmentation method for cardiac MRI to address current segmentation challenges.
- To improve the accuracy and robustness of automated cardiac tissue and organ segmentation.
- To enhance computer-aided diagnosis through precise delineation of cardiac structures.
Main Methods:
- A two-stage convolutional neural network (CNN) segmentation approach combining a Log-Gabor filter attention mechanism and metric classification.
- Log-Gabor filterbank enhances tissue texture and contour information.
- Spatial and channel attention mechanisms with varying kernel sizes adaptively extract features and focus on discriminative information.
- A metric classification network refines segmentation of difficult boundaries.
Main Results:
- The proposed method was validated on a cardiac MRI dataset for segmenting 7 cardiac tissues.
- The approach demonstrated effectiveness in enhancing feature extraction and refining segmentation boundaries.
- Achieved competitive performance compared to existing deep learning-based segmentation models.
Conclusions:
- The proposed two-stage CNN method effectively addresses challenges in cardiac MRI segmentation.
- The integration of Log-Gabor filters, attention mechanisms, and metric classification improves segmentation accuracy, especially for intricate boundaries.
- This method shows significant potential for advancing computer-aided cardiovascular diagnosis.
Abstract:
Accurate segmentation of multiple tissues and organs in cardiac medical imaging is of great value in computer-aided cardiovascular diagnosis. However, it is challenging due to the complex distribution of various tissues and organs in cardiac MRI (magnetic resonance imaging) slices, low discriminative and large spanning organs. To handle these problems, a two-stage CNN (convolutional neural network) segmentation method based on the combination of Log-Gabor filter attention mechanism and metric classification is proposed. The Log-Gabor filterbank is applied to selectively enhance the texture information and contour information of each tissue and organ, and the spatial and channel attention mechanism jointly with the varying kernel size of Log-Gabor filterbank is incorporated into the codec structure to adaptively extract target features of different sizes and focus on the discriminative features in the network. To solve the problem of insufficient segmentation on subtle and adherent edges involving different tissues, a metric classification network is incorporated to finely optimize the hard-to-be-segmented boundaries. The proposed method was tested on cardiac MRI data set to segment 7 cardiac tissues, and the rationality and effectiveness of the method were verified. In comparison to a series of deep learning-based segmentation models, the proposed method achieves competitive performance.
