Performing a minimally invasive surgery comes with a significant advantage regarding
rehabilitating the patient after the operation. But it also causes difficulties, mainly
for the surgeon or expert who performs the surgical intervention, since only visual
information is available and they cannot use their tactile senses during keyhole surgeries.
This is the case with laparoscopic hysterectomy since some organs are also difficult
to distinguish based on visual information, making laparoscope-based hysterectomy
challenging. In this paper, we propose a solution based on semantic segmentation,
which can create pixel-accurate predictions of surgical images and differentiate the
uterine arteries, ureters, and nerves. We trained three binary semantic segmentation
models based on the U-Net architecture with the EfficientNet-b3 encoder; then, we
developed two ensemble techniques that enhanced the segmentation performance. Our
pixel-wise ensemble examines the segmentation map of the binary networks on the lowest
level of pixels. The other algorithm developed is a region-based ensemble technique
that takes this examination to a higher level and makes the ensemble based on every
connected component detected by the binary segmentation networks. We also introduced
and trained a classic multi-class semantic segmentation model as a reference and compared
it to the ensemble-based approaches. We used 586 manually annotated images from 38
surgical videos for this research and published this dataset.
