A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI (1512.07951v1)

Abstract: Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic segmentation tool for the LV from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are utilized to infer the shape of the LV. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets taken from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81mm and 0.86, versus those of 79.2%-95.62%, 0.87-0.9, 1.76-2.97mm and 0.67-0.78, obtained by other methods, respectively.

• The paper introduces a deep-learning and deformable-model approach that achieves a mean Dice metric of 0.94 for LV segmentation.
• It uses CNNs for ROI detection and stacked autoencoders for shape inference to mitigate common segmentation issues like leakage and shrinkage.
• Validation on the MICCAI 2009 dataset demonstrates strong correlations with key clinical indices, enhancing diagnostic reliability.

Overview of the Paper

The paper "A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI" by M. R. Avendi, Arash Kheradvar, and Hamid Jafarkhani presents a methodology integrating deep learning with deformable models to achieve automatic segmentation of the left ventricle (LV) in cardiac MRI datasets. This task is crucial for accurate calculation of clinical indices such as ventricular volume and ejection fraction.

Methodology

The approach consists of three primary stages:

1. Automatic Detection: Convolutional neural networks (CNNs) are utilized to localize the LV, extracting a region of interest (ROI) from the cardiac MRI images. The paper applies a network architecture involving convolutional layers followed by pooling to effectively capture the spatial hierarchies needed for accurate detection.
2. Shape Inferring: The method leverages stacked autoencoders to infer the LV's shape from the identified ROI. This deep learning aspect focuses on training the network to represent the complex anatomical shape of the LV, allowing it to adapt to variations in the dataset.
3. Segmentation and Alignment: By integrating the inferred shape with deformable models, the approach offers enhanced segmentation accuracy. The inferred shape initializes and constrains the deformable model, addressing traditional challenges such as leakage and shrinkage. An alignment process reduces inter-slice misalignment, smoothing 3D reconstructions.

Validation and Performance

The approach was validated using the MICCAI 2009 LV segmentation challenge dataset, demonstrating superior performance over various state-of-the-art methods. Key evaluation metrics included percentage of good contours, Dice metric, average perpendicular distance (APD), and conformity.

• Significant improvements were observed across multiple metrics, as the proposed method achieved a mean Dice metric of 0.94 and APD of 1.81 mm, indicating substantive alignment with manual expert annotations.
• The paper's correlation analysis for clinical indices (EDV, ESV, EF) revealed high correlation coefficients, affirming the method's clinical relevance.

Implications and Future Work

From a practical standpoint, the combined use of deep learning with deformable models offers a robust solution for automated LV segmentation. This framework not only enhances spatial accuracy but also reduces the computational complexity often associated with traditional segmentation models.

Theoretically, this work illustrates the adaptability and potency of deep learning paradigms in medical image analysis, particularly within constrained data environments. Speculative future advancements might involve extending this integration to other cardiac structures and employing larger, diverse datasets to explore further generalization and robustness across varied pathologies.

Overall, the integration of deep learning and traditional modeling techniques in this work underscores a promising direction for improved clinical diagnostics through automated imaging solutions.