Computerized segmentation of pulmonary nodules depicted in CT examinations using freehand sketches.
OBJECTIVE
To aid a consistent segmentation of pulmonary nodules, the authors describe a novel computerized scheme that utilizes a freehand sketching technique and an improved break-and-repair strategy.


METHODS
This developed scheme consists of two primary parts. The first part is freehand sketch analysis, where the freehand sketching not only serves a natural way of specifying the location of a nodule, but also provides a mechanism for inferring adaptive information (e.g., the mass center, the density, and the size) in regard to the nodule. The second part is an improved break-and-repair strategy. The improvement avoids the time-consuming ray-triangle intersections using spherical bins and replaces the original global implicit surface reconstruction with a local implicit surface fitting and blending scheme. The performance of this scheme, including accuracy and consistence, was assessed using 50 CT examinations in the Lung Image Database Consortium (LIDC). For each of these examinations, a single nodule was selected under the aid of a publically available tool to assure these nodules were diverse in size, location, and density. Two radiologists were asked to use the developed tool to segment these nodules twice at different times (at least three months apart). A Hausdorff distance based method was used to assess the discrepancies (agreements) between the computerized results and the results by the four radiologists in the LIDC as well as the inter- and intrareader agreements in freehand sketching.


RESULTS
The maximum and mean discrepancies in boundary outlines between the computerized scheme and the radiologists were 2.73 ± 1.32 mm and 1.01 ± 0.47 mm, respectively. When the nodules were classified (binned) into different size ranges, the maximum errors ranged from 1.91 to 4.13 mm; but smaller nodules had larger percentage discrepancies in term of size. Under the aid of the developed scheme, the inter- and intrareader variability in averaged maximum discrepancy across all types of pulmonary nodules were consistently smaller than 0.15 ± 0.07 mm. The computational cost in time of segmenting a pulmonary nodule ranged from 0.4 to 2.3 s with an average of 1.1 s for a typical desktop computer.


CONCLUSIONS
The experiments showed that this scheme could achieve a reasonable performance in nodule segmentation and demonstrated the merits of incorporating freehand sketching into pulmonary nodule segmentation.
Department of Radiology, The First Affiliated Hosptial of Medical School, Xi'an Jiaotong Unversity, Xi'an City, Shaanxi Province 710061, People's Republic of China.
Yongqian
Y
Qiang
Department of Radiology, The First Affiliated Hosptial of Medical School, Xi'an Jiaotong Unversity, Xi'an City, Shaanxi Province 710061, People's Republic of China.
Qiuping
Q
Wang
Department of Radiology, The First Affiliated Hosptial of Medical School, Xi'an Jiaotong Unversity, Xi'an City, Shaanxi Province 710061, People's Republic of China.
Guiping
G
Xu
Department of Radiology, The First Affiliated Hosptial of Medical School, Xi'an Jiaotong Unversity, Xi'an City, Shaanxi Province 710061, People's Republic of China.
Hongxia
H
Ma
Department of Radiology, The First Affiliated Hosptial of Medical School, Xi'an Jiaotong Unversity, Xi'an City, Shaanxi Province 710061, People's Republic of China.
Lei
L
Deng
Department of Radiology, The First Affiliated Hosptial of Medical School, Xi'an Jiaotong Unversity, Xi'an City, Shaanxi Province 710061, People's Republic of China.
Lei
L
Zhang
Departments of Radiology and Bioengineering, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, Pennsylvania 15213.
Jiantao
J
Pu
Department of Radiology, The First Affiliated Hosptial of Medical School, Xi'an Jiaotong Unversity, Xi'an City, Shaanxi Province 710061, People's Republic of China.
Youmin
Y
Guo
D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*