  Automatic segmentation of medical images with DL algorithms has proven to be
highly successful. With most of these algorithms, inter-observer variation is
an acknowledged problem, leading to sub-optimal results. This problem is even
more significant in post-operative clinical target volume (post-op CTV)
segmentation due to the absence of macroscopic visual tumor in the image. This
study, using post-op CTV segmentation as the test bed, tries to determine if
physician styles are consistent and learnable, if there is an impact of
physician styles on treatment outcome and toxicity; and how to explicitly deal
with physician styles in DL algorithms to facilitate its clinical acceptance. A
classifier is trained to identify which physician has contoured the CTV from
just the contour and corresponding CT scan, to determine if physician styles
are consistent and learnable. Next, we evaluate if adapting automatic
segmentation to physician styles would be clinically feasible based on a lack
of difference between outcomes. For modeling different physician styles of CTV
segmentation, a concept called physician style-aware (PSA) segmentation is
proposed which is an encoder-multidecoder network trained with perceptual loss.
With the proposed physician style-aware network (PSA-Net), Dice similarity
coefficient (DSC) accuracy increases on an average of 3.4% for all physicians
from a general model that is not style adapted. We show that stylistic
contouring variations also exist between institutions that follow the same
segmentation guidelines and show the effectiveness of the proposed method in
adapting to new institutional styles. We observed an accuracy improvement of 5%
in terms of DSC when adapting to the style of a separate institution.
