Spatial similarity metrics

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Spatial similarity metrics

The espadon package calculates several spatial similarity metrics, such as :

  • the volumetric DICE similarity defined by DSC = 2 (VA ∩ VB) / (VA+VB)
  • the Dice-jaccard coefficient defined by DJC = (VA ∩ VB) / (VA ∪ VB)
  • the mean distance to conformity MDC, over-contouring mean distance over.MDC and under-contouring mean distance under.MDC, defined by Jena et al [1]
  • the maximum , mean and quantiles of Hausdorff distances
  • the surface DICE metric defined by Nikolov et al [2]

Metrics using the volumes of regions of interest (ROIs) are calculated using the sp.similarity.from.bin function, and those using surfaces are calculated using the sp.similarity.from.mesh function.

In the following example, the patient’s file contains a CT-scan file and an rt-struct file in which the contours of two regions of interest are to be compared. To calculate the spatial similarity metrics, first generate the binary volumes relative to the ROIs to be compared.

library (espadon)
patient.folder <- choose.dir ()
pat <- load.patient.from.dicom (patient.folder, data = FALSE)

S <- load.obj.data (pat$rtstruct[[1]])
CT <- load.obj.data (pat$ct[[1]])

# The binary objects for 2 ROIs named "roi A" and "roi B" respectively :
bin.A <- bin.from.roi (CT, S, roi.sname = "roi A", alias = "ROI A")
bin.B <- bin.from.roi (CT, S, roi.sname = "roi B", alias = "ROI B")

# The mesh objects for these 2 ROIs:
# FYI: the smooth.iteration argument avoids staircase meshing, thanks to z-axis binning.
#      As a result, ROI contours are also smoothed in XY.
mesh.A <- mesh.from.bin(bin.A, smooth.iteration = 10, alias = "ROI A")
mesh.B <- mesh.from.bin(bin.B, smooth.iteration = 10, alias = "ROI B")

# spatial similarity
sp.similarity.from.bin (bin.A , bin.B)
sp.similarity.from.mesh (mesh.A , mesh.B, hausdorff.quantile = c (0.5, 0.95), surface.DSC.tol = 1:3)

References

[1] Jena R, et al. (2010). “A novel algorithm for the morphometric assessment of radiotherapy treatment planning volumes.” Br J Radiol., 83(985), 44-51. doi:10.1259/bjr/27674581.

[2] Nikolov S, et al. (2018). “Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy.” ArXiv, abs/1809.04430.