  Purpose Automated segmentation of anatomical structures in medical image
analysis is a prerequisite for autonomous diagnosis as well as various computer
and robot aided interventions. Recent methods based on deep convolutional
neural networks (CNN) have outperformed former heuristic methods. However,
those methods were primarily evaluated on rigid, real-world environments. In
this study, existing segmentation methods were evaluated for their use on a new
dataset of transoral endoscopic exploration. Methods Four machine learning
based methods SegNet, UNet, ENet and ErfNet were trained with supervision on a
novel 7-class dataset of the human larynx. The dataset contains 536 manually
segmented images from two patients during laser incisions. The
Intersection-over-Union (IoU) evaluation metric was used to measure the
accuracy of each method. Data augmentation and network ensembling were employed
to increase segmentation accuracy. Stochastic inference was used to show
uncertainties of the individual models. Patient-to-patient transfer was
investigated using patient-specific fine-tuning. Results In this study, a
weighted average ensemble network of UNet and ErfNet was best suited for the
segmentation of laryngeal soft tissue with a mean IoU of 84.7 %. The highest
efficiency was achieved by ENet with a mean inference time of 9.22 ms per
image. It is shown that 10 additional images from a new patient are sufficient
for patient-specific fine-tuning. Conclusion CNN-based methods for semantic
segmentation are applicable to endoscopic images of laryngeal soft tissue. The
segmentation can be used for active constraints or to monitor morphological
changes and autonomously detect pathologies. Further improvements could be
achieved by using a larger dataset or training the models in a self-supervised
manner on additional unlabeled data.
