|
The quantification of radiomics features is impacted by a complex interplay of image quality and segmentation performance. To understand and optimize the process, it is highly beneficial to isolate the impact of each factor quantitatively. The purpose of this study was to develop a segmentation performance index (𝑠′) to describe the segmentation performance relative to an ideal estimation, and further apply the index across several segmentation algorithms and image quality conditions in the task of characterizing lesion morphology. First, an estimator was developed with prior knowledge of the lesion and the imaging resolution properties to represent the best that an ideal segmentation operator would be expected to yield given the information content available from the image. Using three anthropomorphic lesion models of a mean contrast of 600 HU and mean radius of 5 mm, the method was applied to three segmentation operators based on (1) active contour, (2) thresholding, and (3) a commercial vendor offering (Siemens Healthineers). Images of the lesions were simulated in a uniform background for a Siemens Flash filtered back-projection (FBP) B31f reconstruction with noise magnitude ranging from 10 HU to 230 HU. Images were simulated 50 times at each noise magnitude to assess test-retest variability. The segmentation masks were used to calculate a root mean square error (RMSE) across test-retest images for morphology and size radiomics features. The ideal estimation RMSE was calculated for each feature and was divided by the segmentation RMSE resulting in the segmentation performance index (𝑠′). The 𝑠′ values were evaluated as a function of increasing noise. Traditional segmentation quality metrics including dice coefficient, volume similarity, and Hausdorff distance were also calculated for each noise magnitude to compare against the 𝑠′ index. Traditional segmentation quality metrics are not reflective of radiomics performance. The 𝑠′ values, in contrast, were found to be both feature-specific and noisespecific, useful in characterizing the performance of segmentation algorithms in measuring radiomics features. This allows for the choice of segmentation algorithms to yield optimum radiomic performance for a given imaging condition and or protocol.
|
