Deep Learning for Pneumothorax Segmentation and Quantification

Abstract

This thesis investigates the role of deep learning, focusing on convolutional neural networks and encoder-decoder architectures, to enhance medical image segmentation, specifically for pneumothorax identification in chest X-ray images. Pneumothorax, a critical condition characterized by lung collapse, requires precise diagnostics for effective management and treatment. Traditional image segmentation methods sometimes underperform due to the complexity and variability of chest X-ray images. In this study, we proposed PTXSeg-Net, a novel deep learning model for accurate and efficient pneumothorax segmentation. This model incorporates residual blocks and attention mechanisms to improve learning capabilities and is further enhanced by deep supervision, which allows for more effective gradient utilization across all network layers, significantly enhancing its performance over existing segmentation methods. Additionally, transfer learning with pre-trained models on extensive chest X-ray datasets improves feature extraction, addressing imbalanced datasets. Data refinement techniques are also employed to optimize the training outcomes. The performance of PTXSeg-Net outperforms the existing segmentation model in terms of accuracy, dice and Jaccard coefficients, demonstrating significant improvement in the field of pneumothorax segmentation. Moreover, leveraging the predicted masks of pneumothorax, this thesis proposes an algorithm that calculates the pneumothorax ratio. This algorithm provides a quantitative measure of the appearance of pneumothorax relative to the total lung area, offering a metric for radiologists to assess the severity of the condition more accurately. This integration of deep learning models and practical quantification algorithm underscores the significant contribution to the field of medical image analysis.

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