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Updated: Jun 19, 2025
3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse
Published on: May 19, 2015
Fine grained automatic left ventricle segmentation via ROI based Tri-Convolutional neural networks
Gayathri K1, Uma Maheswari N1, Venkatesh R2
1Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India.
Insights
A novel Tri-Convolutional Network (Tri-ConvNets) model accurately segments the left ventricle (LV) in cardiac images. This deep learning approach improves diagnostic accuracy and efficiency for cardiovascular disease assessment.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiology
Background:
- Left Ventricle Segmentation (LVS) is vital for assessing cardiac function and diagnosing cardiovascular diseases.
- Manual LVS is time-consuming, labor-intensive, and can reduce diagnostic accuracy.
- Accurate LVS is essential for measuring key parameters like myocardial mass, end-diastolic volume, and ejection fraction.
Purpose of the Study:
- To develop a highly accurate semantic segmentation method for the left ventricle using deep neural networks.
- To introduce the Tri-Convolutional Network (Tri-ConvNets) model for efficient and precise LVS.
- To overcome the limitations of manual segmentation in cardiac image analysis.
Main Methods:
- Cardiac MRI (CMRI) images undergo pre-processing to reduce noise and enhance quality.
- Region of Interest (ROI)-based extraction is performed in three stages to isolate the left ventricle.
- A combination of deep learning models (standard ConvNet, Fully ConvNet, ConvNets) is employed for feature extraction and pixel-wise segmentation.
Main Results:
- The Tri-ConvNets model achieved high Jaccard indices (0.9491 ± 0.0188 on Sunny Brook, 0.9497 ± 0.0237 on York) and Dice indices (0.9419 ± 0.0178 on ACDC, 0.9414 ± 0.0247 on LVSC).
- Experimental results demonstrate that Tri-ConvNets is faster and requires fewer computational resources than existing state-of-the-art models.
- The model provides accurate segmentation of the left ventricle, crucial for cardiac function assessment.
Conclusions:
- The proposed Tri-ConvNets model offers a significant advancement in automated left ventricle segmentation.
- This deep learning approach enhances diagnostic accuracy and efficiency in cardiovascular imaging.
- Tri-ConvNets presents a computationally efficient and resource-minimal solution for LVS, outperforming current methods.
Background:
The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important attention due to its ability to measure vital parameters such as myocardial mass, end-diastolic volume, and ejection fraction. Medical professionals realize that manually segmenting data to evaluate these processes takes a lot of time, effort when diagnosing heart diseases. Yet, manually segmenting these images is labour-intensive and may reduce diagnostic accuracy.
Objective/Methods:
This paper, propose a combination of different deep neural networks for semantic segmentation of the left ventricle based on Tri-Convolutional Networks (Tri-ConvNets) to obtain highly accurate segmentation. CMRI images are initially pre-processed to remove noise artefacts and enhance image quality, then ROI-based extraction is done in three stages to accurately identify the LV. The extracted features are given as input to three different deep learning structures for segmenting the LV in an efficient way. The contour edges are processed in the standard ConvNet, the contour points are processed using Fully ConvNet and finally the noise free images are converted into patches to perform pixel-wise operations in ConvNets.
Results/Conclusions:
The proposed Tri-ConvNets model achieves the Jaccard indices of 0.9491 ± 0.0188 for the sunny brook dataset and 0.9497 ± 0.0237 for the York dataset, and the dice index of 0.9419 ± 0.0178 for the ACDC dataset and 0.9414 ± 0.0247 for LVSC dataset respectively. The experimental results also reveal that the proposed Tri-ConvNets model is faster and requires minimal resources compared to state-of-the-art models.
