Motivation: For primary and metastatic liver cancer patients undergoing liver resection, a laparoscopic approach can
reduce recovery times and morbidity while offering equivalent curative results; however, only about 10% of tumours
reside in anatomical locations that are currently accessible for laparoscopic resection. Augmenting laparoscopic video
with registered vascular anatomical models from pre-procedure imaging could support using laparoscopy in a wider
population. Segmentation of liver tissue on laparoscopic video supports the robust registration of anatomical liver models
by filtering out false anatomical correspondences between pre-procedure and intra-procedure images. In this paper, we
present a convolutional neural network (CNN) approach to liver segmentation in laparoscopic liver procedure videos.
Method: We defined a CNN architecture comprising fully-convolutional deep residual networks with multi-resolution
loss functions. The CNN was trained in a leave-one-patient-out cross-validation on 2050 video frames from 6 liver
resections and 7 laparoscopic staging procedures, and evaluated using the Dice score.
Results: The CNN yielded segmentations with Dice scores ≥0.95 for the majority of images; however, the inter-patient
variability in median Dice score was substantial. Four failure modes were identified from low scoring segmentations:
minimal visible liver tissue, inter-patient variability in liver appearance, automatic exposure correction, and pathological
liver tissue that mimics non-liver tissue appearance.
Conclusion: CNNs offer a feasible approach for accurately segmenting liver from other anatomy on laparoscopic video,
but additional data or computational advances are necessary to address challenges due to the high inter-patient variability
in liver appearance.