title: Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT.
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.
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA. Electronic address: jzhou@umm.edu.
authors.forename: Jinghao
authors.initials: J
authors.lastname: Zhou
authors.affiliation: Department of Computer Science, Rutgers, The State University of New Jersey, NJ, USA.
authors.forename: Zhennan
authors.initials: Z
authors.lastname: Yan
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Lasio
authors.affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA.
authors.forename: Junzhou
authors.initials: J
authors.lastname: Huang
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
authors.forename: Baoshe
authors.initials: B
authors.lastname: Zhang
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
authors.forename: Navesh
authors.initials: N
authors.lastname: Sharma
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
authors.forename: Karl
authors.initials: K
authors.lastname: Prado
authors.affiliation: Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
authors.forename: Warren
authors.initials: W
authors.lastname: D'Souza
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000033:anatomy & histology* / Q000000981:diagnostic imaging / Q000473:pathology; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods*; D014057:Tomography, X-Ray Computed
keywords: Atlas-based active volume model; Compromised lung segmentation; Image segmentation; Lung cancer; Sparse shape composition