One of the main goals in software solutions for treatment planning is to automatize delineation of organs at risk (OARs). In this pilot feasibility study a clinical validation was made of computed tomography (CT)-based extracranial auto-segmentation (AS) using the Brainlab Anatomical Mapping tool (AM).
The delineation of nine extracranial OARs (lungs, kidneys, trachea, heart, liver, spinal cord, esophagus) from clinical datasets of 24 treated patients was retrospectively evaluated. Manual delineation of OARs was conducted in clinical routine and compared with AS datasets using AM. The Dice similarity coefficient (DSC) and maximum Hausdorff distance (HD) were used as statistical and geometrical measurements, respectively. Additionally, all AS structures were validated using a subjective qualitative scoring system.
All patient datasets investigated were successfully processed with the evaluated AS software. For the left lung (0.97 ± 0.03), right lung (0.97 ± 0.05), left kidney (0.91 ± 0.07), and trachea (0.93 ± 0.04), the DSC was high with low variability. The DSC scores of other organs (right kidney, heart, liver, spinal cord), except the esophagus, ranged between 0.7 and 0.9. The calculated HD values yielded comparable results. Qualitative assessment showed a general acceptance in more than 85% of AS OARs—except for the esophagus.
The Brainlab AM software is ready for clinical use in most of the OARs evaluated in the thoracic and abdominal region. The software generates highly conformal structure sets compared to manual contouring. The current study design needs revision for further research.
Collier D, Burnett SSC, Amin M et al (2002) Assessment of consistency in contouring of normal-tissue anatomic structures. J Appl Clin Med Phys 4:1
Bach Cuadra M, Duay V, Thiran JP (2015) Atlas-based Segmentation. In: Paragios N, Duncan J, Ayache N (eds) Handbook of Biomedical Imaging. Springer, Boston, MA, pp 221–244
Zhu M, Bzdusek K, Brink C et al (2013) Multi-institutional Quantitative Evaluation and Clinical Validation of Smart Probabilistic Image Contouring Engine (SPICE) Autosegmentation of Target Structures and Normal Tissues on Computer Tomography Images in the Head and Neck, Thorax, Liver, and Male Pelvis Areas. Int J Radiation Oncol Biol Phys 87:809–816 CrossRef
Abstracts DEGRO (2018) Strahlentherapie und Onkologie 194 (S1):1–222
- Automatic image segmentation based on synthetic tissue model for delineating organs at risk in spinal metastasis treatment planning
Lars Henrik Sowa
- Springer Berlin Heidelberg
Strahlentherapie und Onkologie
Journal of Radiation Oncology, Biology, Physics
Print ISSN: 0179-7158
Elektronische ISSN: 1439-099X
Neu im Fachgebiet Onkologie
Mail Icon II