ArticleInternational journal of computer assisted radiology and surgery2019
A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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35 citing papers in PubMed, 1 synthesis or guideline pooled it, 108 citations in OpenAlex.
- The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature.Sensors (Basel, Switzerland) · 2022Pooled it
- Development and validation of a deep learning model for identifying high-quality laryngoscopic images.Scientific reports · 2026Article
- Upper Airway Anatomical Landmark Dataset for Automated Bronchoscopy and Intubation.Scientific data · 2025Article
- Optimization of deep learning models for inference in low resource environments.Computers in biology and medicine · 2025Article
- The Laryngovibrogram as a normalized spatiotemporal representation of vocal fold dynamics.Scientific reports · 2025Article
- Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.Journal of imaging informatics in medicine · 2024Article
- Sociodemographic reporting in videomics research: a review of practices in otolaryngology - head and neck surgery.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Review
- A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method.Scientific reports · 2024Article
- Contact Endoscopy - Narrow Band Imaging (CE-NBI) data set for laryngeal lesion assessment.Scientific data · 2023Article
- Instance segmentation of upper aerodigestive tract cancer: site-specific outcomes.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2023Article
- Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images.BMC bioinformatics · 2023Article
- AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome.Sensors (Basel, Switzerland) · 2022Article
- An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images.Sensors (Basel, Switzerland) · 2022Article
- Correlating Perceptual Voice Quality in Adductor Spasmodic Dysphonia With Computer Vision Assessment of Glottal Geometry Dynamics.Journal of speech, language, and hearing research : JSLHR · 2022Article
- A single latent channel is sufficient for biomedical glottis segmentation.Scientific reports · 2022Article
- Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial intelligence in clinical endoscopy: Insights in the field of videomics.Frontiers in surgery · 2022Review
- Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images.Frontiers in oncology · 2022Article
- Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care.PloS one · 2022Article
- Optimal Deep Learning-Based Vocal Fold Disorder Detection and Classification Model on High-Speed Video Endoscopy.Journal of healthcare engineering · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeAutomated 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.
methodsFour 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.
resultsIn 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.
conclusionCNN-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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.