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

Abstract

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.

Fig 1. Comparison of manual (left) and CIP-based (right) segmentation.

Yellow shaded region indicated the disagreement (or region of uncertainty) between contours performed by four radiologists (bottom left) or different CIP-based seed locations (bottom right). In this example, the region of uncertainty for manual segmentation was 3222 ml while the region was only 46 ml for the CIP-based segmentation. dsi_CIP was ≈ 100%, while dsi_manual was 88%.

Fig 2. Robustness (or stability) of the manual and CIP-based segmentation.

The robustness of the manual and CIP-based segmentation assessed with the region of uncertainty (δ) and Dice similarity index (dsi).

Fig 3. Bland-Altman plots.

Bland-Altman plots highlights the differences between $\overline{V_{CIP}}$ and $\overline{V_{manual}}$ for all nodules. The 95% interval of the differences are depicted by the blue dotted lines. Solid red line is the average difference between $\overline{V_{CIP}}$ and $\overline{V_{manual}}$ (= 318ml).

Fig 4. Examples of nodules that were segmented by radiologists manually and CIP segmentations.

a) The robustness of the CIP segmentation was excellent, while substantial interobserver variability was observed in manual segmentation. CIP segmentation was also in excellent agreement with manual contours. However, CIP segmentation was observed to include part of the chest wall (indicated by an arrow) b) Despite being perfectly robust CIP segmentation, it included the region of the normal lung in proximity of the small nodule. c) Cavitation in the center of the nodule. Poor CIP segmentation performance was found. d) Non-solid (ground glass opacity) nodule with poorly defined boundary and subtle appearance is indicated by the red arrow. Poor CIP segmentation performance was found.

Fig 5. The relationships between nodule characteristics, nodule volume, and DSI_Agree.

This figure highlights the relationships between nodule characteristics, nodule volume, and DSI_Agree. Calcification: Solid = solid calcification, Central = central calcification, None = no calcification. Lobulation: None = not lobulated. Spiculated: None = not spiculated. Texture: Mixed = Semi-solid nodules. Malignancy: Unlikely = unlikely for cancer, Suspicious = suspicious for cancer. Nodule Volume: Q1 = 162ml, Q1–Q3 = 162ml to 796ml, and Q3 = 796ml; Q = quantile.