ACCURATE SEGMENTATION FOR PATHOLOGICAL LUNG BASED ON INTEGRATION OF 3D APPEARANCE AND SURFACE MODELS

A novel unsupervised-based segmentation method is introduced to accurately delineate the lung region in 3D CT images based on appearance and geometric models. First, a probabilistic model that utilized a linear combination of Gaussian (LCG) tuned by a modified expectation maximization (EM) algorithm, is employed to model the density distribution of 3D CT chest volume. Subsequently, the initial labeling of the 3D CT chest volume is mapped to a probability distribution based on a 3D Markov Gibbs random field (MGRF) for refining. Finally, a geometric model is employed to refine the proposed segmentation by interpolating/connecting two points on its boundary with high curvature. The effectiveness of the proposed approach on 3D computed tomography (CT) chest scans of $26$ patients diagnosed with different severity of coronavirus disease 2019 (COVID-19) is evaluated using four different metrics: overlap coefficient, Dice similarity coefficient (DSC), absolute lung volume difference (ALVD), and 95th-percentile bidirectional Hausdorff distance (95th HD). The proposed method achieved $94.89%+-2.39%, 97.36%+-1.27%, 1.79+-1.89, and 4.75+-2.3, respectively. Compared to three state-of-the-art methods based on deep learning approaches, the proposed method achieved superior performance in segmenting pathological lung tissues, demonstrating the promising of the proposed segmentation system.