The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights

Abstract

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

Graphical abstract

Figure 1:

Overview of convolutional filters. An image is filtered using convolution to create a filtered image (top). Each image consists of values. At convolution, a filter with three weights (1.0, −2.0, 1.0) is slid across the image, and adjacent image values are multiplied with the corresponding filter values and summed to create a response value for each position in the image. Convolutional filtering is positioned after resampling in the overall radiomics image processing scheme (center). This workflow starts with an image obtained from a repository or archiving system in a digital format. The image is optionally converted (eg, from PET activity to standardized uptake values) and undergoes postprocessing (eg, MRI bias-field correction). Segmentation masks are either loaded in a digital format or automatically created. Both image and segmentation masks are optionally resampled. Filtered images are created by filtering the image. Filtered images and segmentation masks are then used to compute radiomic features. This study attempts to standardize several types of convolution filters (bottom). The original CT image is shown for reference.

Figure 2:

Three filters are used to quantify different characteristics of the peritumoral region in a chest CT, with an out-of-plane tumor. For each filter, mean and maximum intensity are computed within the segmentation masks in three filtered images. The standardized filtered image was created by applying a standardized filter to the original image. The other two filtered images resulted from filter implementations that were not standardized. The Laplacian of Gaussian filter is used to quantify the presence of edges and highlight fine details. The scale of the filter is 2.0 mm, and it is truncated at 8.0 mm. The nonstandardized filters use 2.0 voxels and truncate at one filter scale (2.0 mm). Separable wavelets are designed to quantify image contents for different frequency bands, though in many radiomics analyses they are used to quantify edges. A pair of low-pass and high-pass wavelet kernels is used to filter the image, highlighting edges in the lateral direction. The nonstandardized filters either use an incorrect orientation (ie, low-pass and high-pass kernels were swapped) or are faulty because the first kernel is used for all directions (ie, a pair of low-pass-low-pass wavelet kernels). Gabor filters are used to quantify directional structures (eg, fibrosis and bronchi). The standardized filter used scale and wavelength parameters of 2.0 mm and was oriented under 30°. The nonstandardized filters use an incorrect orientation or express parameters in 2.0 voxels.

Figure 3:

Study overview. Several figure elements adapted, under a CC BY 4.0 license, from reference .

Figure 4:

Results overview. In phase 1, participating teams computed 36 filtered images of convolutional filters according to predefined configurations. These filtered images were compared, and consensus was measured. Teams updated their implementations iteratively, which led to an improvement of consensus over time (arbitrary [arb.] unit; 27 months). Consensus strength was based on matching the voxel-wise difference between filtered images and the tentative reference filtered image within a tolerance. The number of participating teams at each point is shown. In phase 2, participating teams computed 396 features from filtered images of convolutional filters according to predefined filter and image processing configurations. As in phase 1, teams updated their implementations iteratively. Unlike phase 1, improvement in consensus was mostly because of more teams enrolling over time (arbitrary unit; 15 months). Consensus strength was based on the number of teams matching the tentative reference feature value within a tolerance and was assigned according to the same categories as in phase 1. In phase 3, reproducibility of features computed from filtered images was validated. Teams computed 486 features from a public data set of 51 patients with soft-tissue sarcoma that were scanned using CT, fluorine 18 fluorodeoxyglucose (FDG) PET, and T1-weighted (T1w) MRI. Reproducibility was assessed using the lower bound of the 95% CI of the intraclass correlation coefficient: poor, lower bound less than 0.50; moderate, between 0.50 and 0.75; good, between 0.75 and 0.90; excellent, greater than 0.90; and unknown, computed by fewer than two teams.