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Application of the 3D Slicer Chest Imaging Platform Segmentation Algorithm for Large Lung Nodule Delineation

Institution:
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publisher:
Public Library of Science
Publication Date:
Jun-2017
Journal:
PLoS One
Volume Number:
12
Issue Number:
6
Pages:
e0178944
Citation:
PLoS One. 2017 Jun 8;12(6):e0178944.
PubMed ID:
28594880
PMCID:
PMC5464594
Appears in Collections:
SLICER, SPL
Sponsors:
R01 HL116931/HL/NHLBI NIH HHS/United States
U01 CA190234/CA/NCI NIH HHS/United States
U24 CA194354/CA/NCI NIH HHS/United States
Generated Citation:
Yip S.S.F., Parmar C., Blezek D., Estepar R.S.J., Pieper S., Kim J., Aerts H.J.W.L. Application of the 3D Slicer Chest Imaging Platform Segmentation Algorithm for Large Lung Nodule Delineation. PLoS One. 2017 Jun 8;12(6):e0178944. PMID: 28594880. PMCID: PMC5464594.
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PURPOSE: Accurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time consuming and affected by inter-observer variability. We evaluated the robustness and accuracy of a publically available semiautomatic segmentation algorithm that is implemented in the 3D Slicer Chest Imaging Platform (CIP) and compared it with the performance of manual segmentation. METHODS: CT images of 354 manually segmented nodules were downloaded from the LIDC database. Four radiologists performed the manual segmentation and assessed various nodule characteristics. The semiautomatic CIP segmentation was initialized using the centroid of the manual segmentations, thereby generating four contours for each nodule. The robustness of both segmentation methods was assessed using the region of uncertainty (δ) and Dice similarity index (DSI). The robustness of the segmentation methods was compared using the Wilcoxon-signed rank test (pWilcoxon<0.05). The Dice similarity index (DSIAgree) between the manual and CIP segmentations was computed to estimate the accuracy of the semiautomatic contours. RESULTS: The median computational time of the CIP segmentation was 10 s. The median CIP and manually segmented volumes were 477 ml and 309 ml, respectively. CIP segmentations were significantly more robust than manual segmentations (median δCIP = 14ml, median dsiCIP = 99% vs. median δmanual = 222ml, median dsimanual = 82%) with pWilcoxon~10-16. The agreement between CIP and manual segmentations had a median DSIAgree of 60%. While 13% (47/354) of the nodules did not require any manual adjustment, minor to substantial manual adjustments were needed for 87% (305/354) of the nodules. CIP segmentations were observed to perform poorly (median DSIAgree≈50%) for non-/sub-solid nodules with subtle appearances and poorly defined boundaries. CONCLUSION: Semi-automatic CIP segmentation can potentially reduce the physician workload for 13% of nodules owing to its computational efficiency and superior stability compared to manual segmentation. Although manual adjustment is needed for many cases, CIP segmentation provides a preliminary contour for physicians as a starting point.

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