Spline curve deformation model with prior shapes for identifying adhesion boundaries between large lung tumors and tissues around lungs in CT images.

Purpose: Automated segmentation of lung tumors attached to anatomic structures such as the chest wall or mediastinum remains a technical challenge because of the similar Hounsfield units of these structures. To address this challenge, we propose herein a spline curve deformation model that combines prior shapes to correct large spatially contiguous errors (LSCEs) in input shapes derived from image-appearance cues.The model is then used to identify the adhesion boundaries between large lung tumors and tissue around the lungs.

Methods: The deformation of the whole curve is driven by the transformation of the control points (CPs) of the spline curve, which are influenced by external and internal forces. The external force drives the model to fit the positions of the non-LSCEs of the input shapes while the internal force ensures the local similarity of the displacements of the neighboring CPs. The proposed model corrects the gross errors in the lung input shape caused by large lung tumors, where the initial lung shape for the model is inferred from the training shapes by shape group-based sparse prior information and the input lung shape is inferred by adaptive-thresholding-based segmentation followed by morphological refinement.

Results: The accuracy of the proposed model is verified by applying it to images of lungs with either moderate large-sized (ML) tumors or giant large-sized (GL) tumors. The quantitative results in terms of the averages of the dice similarity coefficient (DSC) and the Jaccard similarity index (SI) are 0.982 ± 0.006 and 0.965 ± 0.012 for segmentation of lungs adhered by ML tumors, and 0.952 ± 0.048 and 0.926 ± 0.059 for segmentation of lungs adhered by GL tumors, which give 0.943 ± 0.021 and 0.897 ± 0.041 for segmentation of the ML tumors, and 0.907 ± 0.057 and 0.888 ± 0.091 for segmentation of the GL tumors, respectively. In addition, the bidirectional Hausdorff distances are 5.7 ± 1.4 and 11.3 ± 2.5 mm for segmentation of lungs with ML and GL tumors, respectively.

Conclusions: When combined with prior shapes, the proposed spline curve deformation can deal with large spatially consecutive errors in object shapes obtained from image-appearance information. We verified this method by applying it to the segmentation of lungs with large tumors adhered to the tissue around the lungs and the large tumors. Both the qualitative and quantitative results are more accurate and repeatable than results obtained with current state-of-the-art techniques.