Combining Image- and Geometric-based Deep Learning for Shape Regression:
  A Comparison to Pixel-level Methods for Segmentation in Chest X-Ray
Ron Keuth Mattias Heinrich
When solving a segmentation task, shaped-base methods can be beneficial
compared to pixelwise classification due to geometric understanding of the
target object as shape, preventing the generation of anatomical implausible
predictions in particular for corrupted data. In this work, we propose a novel
hybrid method that combines a lightweight CNN backbone with a geometric neural
network (Point Transformer) for shape regression. Using the same CNN encoder,
the Point Transformer reaches segmentation quality on per with current
state-of-the-art convolutional decoders ($4\pm1.9$ vs $3.9\pm2.9$ error in mm
and $85\pm13$ vs $88\pm10$ Dice), but crucially, is more stable w.r.t image
distortion, starting to outperform them at a corruption level of 30%.
Furthermore, we include the nnU-Net as an upper baseline, which has $3.7\times$
more trainable parameters than our proposed method.