ReviewBiomedical engineering letters2022
Automated segmentation of the larynx on computed tomography images: a review.
Review in Biomedical engineering letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 26 citations in OpenAlex.
- Laryngeal involvement in relapsing polychondritis: clinical and CT findings in 173 patients.RMD open · 2025Article
- Energy estimation methods for positron emission tomography detectors composed of multiple scintillators.Biomedical engineering letters · 2025Article
- Strategies for mitigating inter-crystal scattering effects in positron emission tomography: a comprehensive review.Biomedical engineering letters · 2024Review
- Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization.Nuclear medicine and molecular imaging · 2024Article
- Exploring the Impact of Model Complexity on Laryngeal Cancer Detection.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Computer-Aided Diagnosis of Laryngeal Cancer Based on Deep Learning with Laryngoscopic Images.Diagnostics (Basel, Switzerland) · 2023Article
- Clinical assessment of a novel machine-learning automated contouring tool for radiotherapy planning.Journal of applied clinical medical physics · 2023Article
- Exploring Radiomics for Classification of Supraglottic Tumors: A Pilot Study in a Tertiary Care Center.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2023Article
- Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.Entropy (Basel, Switzerland) · 2022Article
- An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The larynx, or the voice-box, is a common site of occurrence of Head and Neck cancers. Yet, automated segmentation of the larynx has been receiving very little attention. Segmentation of organs is an essential step in cancer treatment-planning. Computed Tomography scans are routinely used to assess the extent of tumor spread in the Head and Neck as they are fast to acquire and tolerant to some movement. This paper reviews various automated detection and segmentation methods used for the larynx on Computed Tomography images. Image registration and deep learning approaches to segmenting the laryngeal anatomy are compared, highlighting their strengths and shortcomings. A list of available annotated laryngeal computed tomography datasets is compiled for encouraging further research. Commercial software currently available for larynx contouring are briefed in our work. We conclude that the lack of standardisation on larynx boundaries and the complexity of the relatively small structure makes automated segmentation of the larynx on computed tomography images a challenge. Reliable computer aided intervention in the contouring and segmentation process will help clinicians easily verify their findings and look for oversight in diagnosis. This review is useful for research that works with artificial intelligence in Head and Neck cancer, specifically that deals with the segmentation of laryngeal anatomy. Supplementary Information: The online version contains supplementary material available at 10.1007/s13534-022-00221-3.
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