Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

PLoS One

Daniel Blezek¹  Chintan Parmar²  Stephen S. F. Yip²  Hugo J. W. L. Aerts²  Raul San Jose Estepar³  John Kim⁴  Steve Pieper⁵ 

[1] Biomedical Engineering Department, Mayo Graduate School of Medicine Rochester, MN, United States of America;Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA, United States of America;Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States of America;Department of Radiology, University of Michigan Health System, Ann Arbor MI, United States of America;Isomics, Inc., Cambridge, MA, United States of America

关键词: Radiologists;    Pulmonary imaging;    Algorithms;    Lung and intrathoracic tumors;    Calcification;    Morphogenic segmentation;    Computed axial tomography;    Physicians;   

DOI  :  10.1371/journal.pone.0178944

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

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.

【 授权许可】

CC BY