Fine grained automatic left ventricle segmentation via ROI based Tri-Convolutional neural networks.

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.