Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT. (December 2015)

• Record Type: Journal Article
• Authors: Zhou, Jinghao; Yan, Zhennan; Lasio, Giovanni; Huang, Junzhou; Zhang, Baoshe; Sharma, Navesh; Prado, Karl; D'Souza, Warren
• Is Part Of: Computerized medical imaging and graphics. Volume 46:Part 1(2015)
• Page Start: 47
• Page End: 55
• Publication Date: 2015-12
• Subjects: Image segmentation -- Lung cancer -- Compromised lung segmentation -- Sparse shape composition -- Atlas-based active volume model; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2015.07.003
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract : Highlights: We proposed the robust shape atlas built upon the output of SSC. The 3D AVM with SSC prior is applied on compromised lung segmentation. Our proposed method can achieve better segmentation accuracy. Abstract: To resolve challenges in image segmentation in oncologic patients with severely compromised lung, we propose an automated right lung segmentation framework that uses a robust, atlas-based active volume model with a sparse shape composition prior. The robust atlas is achieved by combining the atlas with the output of sparse shape composition. Thoracic computed tomography images ( n = 38) from patients with lung tumors were collected. The right lung in each scan was manually segmented to build a reference training dataset against which the performance of the automated segmentation method was assessed. The quantitative results of this proposed segmentation method with sparse shape composition achieved mean Dice similarity coefficient (DSC) of (0.72, 0.81) with 95% CI, mean accuracy (ACC) of (0.97, 0.98) with 95% CI, and mean relative error (RE) of (0.46, 0.74) with 95% CI. Both qualitative and quantitative comparisons suggest that this proposed method can achieve better segmentation accuracy with less variance than other atlas-based segmentation methods in the compromised lung segmentation.