Healthc Inform Res.  2016 Oct;22(4):305-315. 10.4258/hir.2016.22.4.305.

Evaluation of Semi-automatic Segmentation Methods for Persistent Ground Glass Nodules on Thin-Section CT Scans

• Kim YJ
• Lee SH
• Park CM
• Kim KG

• KMID: 2357385
• DOI: http://doi.org/10.4258/hir.2016.22.4.305

Abstract

OBJECTIVES
This work was a comparative study that aimed to find a proper method for accurately segmenting persistent ground glass nodules (GGN) in thin-section computed tomography (CT) images after detecting them.
METHODS
To do this, we first applied five types of semi-automatic segmentation methods (i.e., level-set-based active contour model, localized region-based active contour model, seeded region growing, K-means clustering, and fuzzy C-means clustering) to preprocessed GGN images, respectively. Then, to measure the similarities, we calculated the Dice coefficient of the segmented area using each semiautomatic method with the result of the manually segmented area by two radiologists.
RESULTS
Comparison experiments were performed using 40 persistent GGNs. In our experiment, the mean Dice coefficient for each semiautomatic segmentation tool with manually segmented area was 0.808 for the level-set-based active contour model, 0.8001 for the localized region-based active contour model, 0.629 for seeded region growing, 0.7953 for K-means clustering, and 0.7999 for fuzzy C-means clustering, respectively.
CONCLUSIONS
The level-set-based active contour model algorithm showed the best performance, which was most similar to the result of manual segmentation by two radiologists. From the differentiation between the normal parenchyma and the nodule, it was also the most efficient. Effective segmentation methods will be essential for the development of computer-aided diagnosis systems for more accurate early diagnosis and prognosis of lung cancer in thin-section CT images.

Keyword

Lung; Computer-Assisted Image Processing; Solitary Pulmonary Nodule; Computer-Assisted Diagnosis; X-Ray Computed Tomography Scanners

MeSH Terms

Diagnosis
Diagnosis, Computer-Assisted
Early Diagnosis
Glass*
Image Processing, Computer-Assisted
Lung
Lung Neoplasms
Methods*
Prognosis
Solitary Pulmonary Nodule
Tomography Scanners, X-Ray Computed
Tomography, X-Ray Computed*

Figure

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  Figure 1 Overall procedure of the study.

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  Figure 2 Two examples of manual segmentation in axial 1 mm section CT (computed tomography) images: (A) CT scan of a 64 year-old man and (B) another CT scan of a 70 year-old woman. All scans were magnified for the purpose of visualization.

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  Figure 3 An example of evaluation for segmentation accuracy with the result of manual segmentation (red contour) #1. (A) Seeded region growing (SRG, black), (B) K-means clustering (KMN, yellow), (C) fuzzy C-means clustering (FCM, white), (D) level-set based active contour model (Level-set based ACM, green), (E) localized region-based active contour model (Localized ACM, blue), and (F) overall comparison of accuracy for each method with boundary overlay.

• 

  Figure 4 An example of evaluation for segmentation accuracy with the result of manual segmentation (red contour) #2. (A) Seeded region growing (SRG, black), (B) K-means clustering (KMN, yellow), (C) fuzzy C-means clustering (FCM, white), (D) level-set based active contour model (Level-set based ACM, green), (E) localized region-based active contour model (Localized ACM, blue), and (F) overall comparison of accuracy for each method with boundary overlay.

• 

  Figure 5 The result of the ROC analysis. Each line is represented in different colors according to the method as follows: the level-set based active contour model (LevelSet_ACM, red), the localized region-based active contour model (Localized_ACM, black dotted), the seeded region growing (SRG, green), the K-means clustering (KMEANS, blue dotted), and the fuzzy C-means clustering (FCM, yellow dotted).

• 

  Figure 6 An example of over-segmentation and under-segmentation: (A) under segmented region in the segmentation with the level-set based active contour model (yellow regions); (B) over segmented region in the segmentation with the seeded region growing (red regions).

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