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

Jinghao Zhou, Zhennan Yan, Giovanni Lasio, Junzhou Huang, Baoshe Zhang, Navesh Sharma, Karl Prado, Warren D'Souza

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)47-55
Number of pages9
JournalComputerized Medical Imaging and Graphics
Volume46
DOIs
StatePublished - Dec 1 2015

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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