  Bolus segmentation is crucial for the automated detection of swallowing
disorders in videofluoroscopic swallowing studies (VFSS). However, it is
difficult for the model to accurately segment a bolus region in a VFSS image
because VFSS images are translucent, have low contrast and unclear region
boundaries, and lack color information. To overcome these challenges, we
propose PECI-Net, a network architecture for VFSS image analysis that combines
two novel techniques: the preprocessing ensemble network (PEN) and the cascaded
inference network (CIN). PEN enhances the sharpness and contrast of the VFSS
image by combining multiple preprocessing algorithms in a learnable way. CIN
reduces ambiguity in bolus segmentation by using context from other regions
through cascaded inference. Moreover, CIN prevents undesirable side effects
from unreliably segmented regions by referring to the context in an asymmetric
way. In experiments, PECI-Net exhibited higher performance than four recently
developed baseline models, outperforming TernausNet, the best among the
baseline models, by 4.54\% and the widely used UNet by 10.83\%. The results of
the ablation studies confirm that CIN and PEN are effective in improving bolus
segmentation performance.
