Format

Send to

Choose Destination
Sci Rep. 2013 Dec 18;3:3529. doi: 10.1038/srep03529.

Volumetric CT-based segmentation of NSCLC using 3D-Slicer.

Author information

1
1] Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA [2] Department of Radiation Oncology (MAASTRO), GROW Research Institute, Maastricht University, Maastricht, the Netherlands [3].
2
1] Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA [2] University of Massachusetts, Lowell, MA, USA.
3
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
4
Department of Radiation Oncology (MAASTRO), GROW Research Institute, Maastricht University, Maastricht, the Netherlands.
5
Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
6
Radiation Oncology, University Hospitals Leuven/KU Leuven, Leuven, Belgium.
7
1] Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA [2] Department of Radiation Oncology (MAASTRO), GROW Research Institute, Maastricht University, Maastricht, the Netherlands.

Abstract

Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the "gold standard". The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81-0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck.

PMID:
24346241
PMCID:
PMC3866632
DOI:
10.1038/srep03529
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center