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Computerized segmentation of pulmonary nodules depicted in CT examinations using freehand sketches.

ABSTRACT: 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.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.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.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.

SUBMITTER: Qiang Y

PROVIDER: S-EPMC3971825 | biostudies-other | 2014 Apr

REPOSITORIES: biostudies-other

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Project description:It is challenging to reproducibly measure and compare cancer lesions on numerous follow-up studies; the process is time-consuming and error-prone. In this paper, we show a method to automatically and reproducibly identify and segment abnormal lymph nodes in serial computed tomography (CT) exams.Our method leverages initial identification of enlarged (abnormal) lymph nodes in the baseline scan. We then identify an approximate region for the node in the follow-up scans using nonrigid image registration. The baseline scan is also used to locate regions of normal, non-nodal tissue surrounding the lymph node and to map them onto the follow-up scans, in order to reduce the search space to locate the lymph node on the follow-up scans. Adaptive region-growing and clustering algorithms are then used to obtain the final contours for segmentation. We applied our method to 24 distinct enlarged lymph nodes at multiple time points from 14 patients. The scan at the earlier time point was used as the baseline scan to be used in evaluating the follow-up scan, resulting in 70 total test cases (e.g., a series of scans obtained at 4 time points results in 3 test cases). For each of the 70 cases, a "reference standard" was obtained by manual segmentation by a radiologist. Assessment according to response evaluation criteria in solid tumors (RECIST) using our method agreed with RECIST assessments made using the reference standard segmentations in all test cases, and by calculating node overlap ratio and Hausdorff distance between the computer and radiologist-generated contours.Compared to the reference standard, our method made the correct RECIST assessment for all 70 cases. The average overlap ratio was 80.7 ± 9.7% s.d., and the average Hausdorff distance was 3.2 ± 1.8 mm s.d. The concordance correlation between automated and manual segmentations was 0.978 (95% confidence interval 0.962, 0.984). The 100% agreement in our sample between our method and the standard with regard to RECIST classification suggests that the true disagreement rate is no more than 6%.Our automated lymph node segmentation method achieves excellent overall segmentation performance and provides equivalent RECIST assessment. It potentially will be useful to streamline and improve cancer lesion measurement and tracking and to improve assessment of cancer treatment response.

Project description:Major issues in the implementation of screening for lung cancer by means of low-dose computed tomography (CT) are the definition of a positive result and the management of lung nodules detected on the scans. We conducted a population-based prospective study to determine factors predicting the probability that lung nodules detected on the first screening low-dose CT scans are malignant or will be found to be malignant on follow-up.We analyzed data from two cohorts of participants undergoing low-dose CT screening. The development data set included participants in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). The validation data set included participants involved in chemoprevention trials at the British Columbia Cancer Agency (BCCA), sponsored by the U.S. National Cancer Institute. The final outcomes of all nodules of any size that were detected on baseline low-dose CT scans were tracked. Parsimonious and fuller multivariable logistic-regression models were prepared to estimate the probability of lung cancer.In the PanCan data set, 1871 persons had 7008 nodules, of which 102 were malignant, and in the BCCA data set, 1090 persons had 5021 nodules, of which 42 were malignant. Among persons with nodules, the rates of cancer in the two data sets were 5.5% and 3.7%, respectively. Predictors of cancer in the model included older age, female sex, family history of lung cancer, emphysema, larger nodule size, location of the nodule in the upper lobe, part-solid nodule type, lower nodule count, and spiculation. Our final parsimonious and full models showed excellent discrimination and calibration, with areas under the receiver-operating-characteristic curve of more than 0.90, even for nodules that were 10 mm or smaller in the validation set.Predictive tools based on patient and nodule characteristics can be used to accurately estimate the probability that lung nodules detected on baseline screening low-dose CT scans are malignant. (Funded by the Terry Fox Research Institute and others; ClinicalTrials.gov number, NCT00751660.).

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Computerized segmentation of pulmonary nodules depicted in CT examinations using freehand sketches.

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