Markus J Ankenbrand1, Liliya Shainberg1, Michael Hock1, David Lohr1, and Laura Maria Schreiber1
1Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Würzburg, Germany
1Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Würzburg, Germany
Sensitivity
analysis reveals differential sensitivity of pathological classes to
basic image transformations for a published deep learning
segmentation model.
Overlay of predicted segmentation masks over transformed versions of an input
image. Each row has images with the same transformation
but different parameters (e.g. first row rotations by different
angles). The ground truth segmentation mask for this image is shown
in Figure 1.
Quantitative
effect on the dice score for each class over the parameter space of
three transformations. A: Rotation, positive values denote a rotation
counter-clockwise while negative ones denote a rotation clockwise. B:
Zoom, a value of 480px is the default scale. Larger values mean
zooming out while smaller values mean zooming in. C: Brightness, a
value of 0.5 denotes no change in brightness while a value of 0 means
completely dark (all black) and 1 means full brightness (all white).
