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Updated: Aug 1, 2025
3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse
Published on: May 19, 2015
Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation
Zi Ye1,2, Yogan Jaya Kumar2, Fengyan Song3
1School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou 325035, China.
Insights
This study introduces Bi-DCNet, a novel deep learning model for automated left ventricular segmentation in echocardiography. The Bi-DCNet effectively segments cardiac images, improving diagnostic accuracy and reducing manual labor.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiology
Background:
- Left ventricular segmentation is crucial for assessing cardiac function using echocardiography.
- Manual segmentation is time-consuming and prone to observer bias.
- Deep learning offers automated segmentation but often overlooks semantic information.
Purpose of the Study:
- To develop an automated deep learning model for accurate left ventricular segmentation in echocardiography.
- To address the limitations of existing deep learning methods by incorporating semantic information.
- To evaluate the proposed model on a large clinical dataset.
Main Methods:
- A novel deep neural network architecture, Bi-DCNet, based on BiSeNet was proposed.
- The model features a spatial path for low-level features and a context path for high-level semantic features.
- Dilated convolutions were integrated for enhanced multi-scale feature extraction.
Main Results:
- The Bi-DCNet model achieved a Dice Similarity Coefficient (DSC) of 0.9228 and an Intersection over Union (IoU) of 0.8576.
- The model demonstrated effectiveness on the EchoNet-Dynamic dataset, a large clinical video dataset.
- This marks the first implementation of a bilateral-structured network for left ventricular segmentation on this dataset.
Conclusions:
- The proposed Bi-DCNet model provides an effective solution for automated left ventricular segmentation in echocardiography.
- The integration of spatial and contextual paths with dilated convolutions improves segmentation accuracy.
- The study highlights the potential of advanced deep learning architectures for cardiac image analysis.
Abstract:
Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads to observer bias. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, on the downside, it still ignores the contribution of all semantic information through the segmentation process. This study proposes a deep neural network architecture based on BiSeNet, named Bi-DCNet. This model comprises a spatial path and a context path, with the former responsible for spatial feature (low-level) acquisition and the latter responsible for contextual semantic feature (high-level) exploitation. Moreover, it incorporates feature extraction through the integration of dilated convolutions to achieve a larger receptive field to capture multi-scale information. The EchoNet-Dynamic dataset was utilized to assess the proposed model, and this is the first bilateral-structured network implemented on this large clinical video dataset for accomplishing the segmentation of the left ventricle. As demonstrated by the experimental outcomes, our method obtained 0.9228 and 0.8576 in DSC and IoU, respectively, proving the structure's effectiveness.
