Hierarchical Hybrid Networks for Automatic Pulmonary Blood Vessel Segmentation in Computed Tomography Images

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Pulmonary arterial hypertension (PAH) is a significant cardiovascular disease requiring accurate diagnosis via medical imaging.
• Deep learning in medical image processing offers advancements but faces challenges due to patient data privacy concerns.
• Balancing the need for large datasets in deep learning with stringent privacy requirements is a critical issue for medical institutions.

Purpose of the Study:

• To develop an automated method for segmenting pulmonary blood vessels from CT scans.
• To address the privacy dilemma in building data-driven deep learning models for medical diagnosis.
• To propose a solution that enables high-quality data utilization while ensuring patient data security.

Main Methods:

• A hierarchical hybrid automatic segmentation model was developed.
• The model integrates local learning and federated learning approaches.
• The model is designed for segmenting pulmonary blood vessels from computed tomography (CT) images.

Main Results:

• The proposed model successfully automates the segmentation of pulmonary blood vessels from raw CT data.
• The federated learning approach demonstrated impressive performance in achieving accurate segmentation.
• The model effectively protects patient data privacy throughout the learning process.

Conclusions:

• The developed model offers an effective solution for pulmonary blood vessel segmentation in PAH diagnosis.
• Federated learning provides a viable strategy for developing data-driven medical AI without compromising patient privacy.
• This approach facilitates the creation of robust deep learning models in healthcare by overcoming data privacy barriers.