The purpose of this study was to develop and validate a semi-automatic segmentation method for thoracic cavity volumetry and mediastinal fat quantification in patients with chronic obstructive pulmonary disease (COPD). The method involved segmenting multiple organs, including the rib, lung, heart, and diaphragm, to separate the thoracic cavity region. A three-dimensional surface-fitting method was used to model the inner thoracic wall and diaphragm, taking into account lung disease-induced variations. The accuracy of the algorithm was evaluated by comparing its results to manual segmentations performed by two expert radiologists. The proposed method was also compared to three other segmentation methods using various evaluation metrics. The results showed that the proposed method achieved high accuracy, outperforming the other methods in terms of volumetric overlap ratio, surface distances, and false positive/negative ratios. The study concluded that the semi-automatic segmentation method can accurately extract multiple organs within the thoracic cavity and may have clinical utility. The method has been implemented in AVIEW Lung Lobe Segmentation by Coreline Soft.