Cardiac Multi-detector CT Segmentation Based on Multiscale Directional Edge Detector and 3D Level Set

Sofia Antunes, Antonio Esposito, Anna Palmisano, Caterina Colantoni, Sergio Cerutti, Giovanna Rizzo

Research output: Contribution to journalArticlepeer-review


Extraction of the cardiac surfaces of interest from multi-detector computed tomographic (MDCT) data is a pre-requisite step for cardiac analysis, as well as for image guidance procedures. Most of the existing methods need manual corrections, which is time-consuming. We present a fully automatic segmentation technique for the extraction of the right ventricle, left ventricular endocardium and epicardium from MDCT images. The method consists in a 3D level set surface evolution approach coupled to a new stopping function based on a multiscale directional second derivative Gaussian filter, which is able to stop propagation precisely on the real boundary of the structures of interest. We validated the segmentation method on 18 MDCT volumes from healthy and pathologic subjects using manual segmentation performed by a team of expert radiologists as gold standard. Segmentation errors were assessed for each structure resulting in a surface-to-surface mean error below 0.5 mm and a percentage of surface distance with errors less than 1 mm above 80%. Moreover, in comparison to other segmentation approaches, already proposed in previous work, our method presented an improved accuracy (with surface distance errors less than 1 mm increased of 8–20% for all structures). The obtained results suggest that our approach is accurate and effective for the segmentation of ventricular cavities and myocardium from MDCT images.
Original languageEnglish
Pages (from-to)1487 - 1501
Number of pages15
JournalAnnals of Biomedical Engineering
Issue number5
Publication statusPublished - May 1 2016


  • Cardiac segmentation
  • Edge detection
  • Filter bank
  • Level set

ASJC Scopus subject areas

  • Biomedical Engineering


Dive into the research topics of 'Cardiac Multi-detector CT Segmentation Based on Multiscale Directional Edge Detector and 3D Level Set'. Together they form a unique fingerprint.

Cite this