Lungs lobes
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Summary
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These studies suggest that various automated and deep learning methods for lung lobe segmentation in CT scans are effective, even in challenging conditions, and are crucial for early pathology detection, surgical planning, and clinical workflows.
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Understanding Lung Lobes: Anatomy, Variations, and Segmentation Techniques
Anatomy of Lung Lobes
The human lungs are divided into five distinct lobes, separated by pulmonary fissures. The right lung consists of three lobes: superior, middle, and inferior, divided by the oblique and horizontal fissures. The left lung has two lobes: superior and inferior, separated by an oblique fissure . These fissures allow for the expansion and contraction of the lobes during respiration and act as barriers to prevent the spread of diseases within the lungs .
Variations in Lung Lobes and Fissures
Variations in the anatomy of lung lobes and fissures are common. Studies have shown that fissures can be complete, incomplete, or absent. For instance, the horizontal fissure in the right lung is often incomplete or absent in a significant number of cases . Accessory fissures and lobes are also observed, which can complicate surgical procedures and radiological interpretations . Knowledge of these variations is crucial for surgeons and radiologists to avoid misinterpretations and to plan effective treatments .
Importance of Lung Lobe Segmentation
Accurate segmentation of lung lobes in computed tomography (CT) scans is essential for diagnosing and treating pulmonary diseases. Segmentation helps in the regional functional analysis of the lungs and in planning surgical interventions . It is particularly important in cases of chronic obstructive pulmonary disease (COPD) and other severe lung conditions where the anatomy may be significantly altered .
Techniques for Lung Lobe Segmentation
Anatomy-Guided Segmentation
One method for lung lobe segmentation involves using anatomical information from the airway and vascular trees to approximate the fissures. This method employs a watershed transform on a distance map of the vasculature, followed by a 3-D graph search to find the optimal surface within the region of interest (ROI). This approach is effective in identifying incomplete fissures and extrapolating them to complete the segmentation.
Ridge Surface Sampling and Shape Model Fitting
Another technique uses ridge surface image features to indicate fissure locations and employs a particle system to sample these surfaces. Principal component analysis (PCA) is then used to fit shape models to the sampled data, distinguishing between fissure and non-fissure candidates. This method has shown high accuracy in segmenting lung lobes, even in cases with incomplete fissures and severe emphysema.
Relational Modeling with Neural Networks
Recent advancements include the use of convolutional neural networks (CNNs) to capture the structured relationships between different lung structures. The RTSU-Net model, for example, leverages visual and geometric relationships to improve segmentation accuracy, particularly in cases of severe lung infections like COVID-19.
Implicit Surface Fitting
Implicit surface fitting using radial basis functions (RBFs) is another approach that represents fissures as implicit functions. This method can seamlessly interpolate gaps in detected fissures and extrapolate the fissure surfaces to the lung boundaries, resulting in a natural segmentation of lung lobes.
Fully Automated 3D U-Net Segmentation
A fully automated method using a 3D U-Net architecture has also been developed. This deep learning-based approach has shown high accuracy in segmenting lung lobes, even in the presence of incomplete or fake fissures. The method is robust and can be adapted for use in clinical workflows for patients with severe lung diseases.
Conclusion
Understanding the anatomy and variations of lung lobes is crucial for accurate diagnosis and treatment of pulmonary diseases. Advances in segmentation techniques, ranging from anatomy-guided methods to deep learning models, have significantly improved the accuracy and efficiency of lung lobe segmentation in CT scans. These developments are essential for enhancing the regional functional analysis of the lungs and for planning effective surgical interventions.
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