Automated Assessment of Critical View of Safety in Laparoscopic
  Cholecystectomy
Yunfan Li Himanshu Gupta Haibin Ling IV Ramakrishnan Prateek Prasanna Georgios Georgakis Aaron Sasson
Cholecystectomy (gallbladder removal) is one of the most common procedures in
the US, with more than 1.2M procedures annually. Compared with classical open
cholecystectomy, laparoscopic cholecystectomy (LC) is associated with
significantly shorter recovery period, and hence is the preferred method.
However, LC is also associated with an increase in bile duct injuries (BDIs),
resulting in significant morbidity and mortality. The primary cause of BDIs
from LCs is misidentification of the cystic duct with the bile duct. Critical
view of safety (CVS) is the most effective of safety protocols, which is said
to be achieved during the surgery if certain criteria are met. However, due to
suboptimal understanding and implementation of CVS, the BDI rates have remained
stable over the last three decades. In this paper, we develop deep-learning
techniques to automate the assessment of CVS in LCs. An innovative aspect of
our research is on developing specialized learning techniques by incorporating
domain knowledge to compensate for the limited training data available in
practice. In particular, our CVS assessment process involves a fusion of two
segmentation maps followed by an estimation of a certain region of interest
based on anatomical structures close to the gallbladder, and then finally
determination of each of the three CVS criteria via rule-based assessment of
structural information. We achieved a gain of over 11.8% in mIoU on relevant
classes with our two-stream semantic segmentation approach when compared to a
single-model baseline, and 1.84% in mIoU with our proposed Sobel loss function
when compared to a Transformer-based baseline model. For CVS criteria, we
achieved up to 16% improvement and, for the overall CVS assessment, we achieved
5% improvement in balanced accuracy compared to DeepCVS under the same
experiment settings.