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.
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.
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.
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.
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We thank Giorgio Peretti from the Ospedale Policlinico San Martino, University of Genova, Italy, for providing us with the in vivo laryngeal data used in this study. We would also like to thank James Napier from the Institute of Lasers and Optics, University of Applied Sciences Emden-Leer, Germany, for his thorough proofreading of this manuscript.
This research has received funding from the European Union as being part of the ERFE OPhonLas project.
Conflict of interest
The authors declare that they have no conflict of interest.
The endoscopic video images were acquired by Prof. Giorgio Peretti (Director of Otorhinolaryngology at Ospedale Policlinico San Martino, University of Genova). Patients gave their written consent for the procedure and the use of the data. No further approval is necessary for such endoscopic recordings. The videos were anonymized and made available inside the \(\upmu \)RALP consortium for further usage.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
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Laves, MH., Bicker, J., Kahrs, L.A. et al. A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation. Int J CARS 14, 483–492 (2019). https://doi.org/10.1007/s11548-018-01910-0
- Computer vision
- Vocal folds
- Soft tissue
- Open-access dataset
- Machine learning
- Patient-to-patient fine-tuning