Automatic workflow for narrow-band laryngeal video stitching

Moccia, Sara; Penza, Veronica; Vanone, Gabriele Omodeo; Momi, Elena De; Mattos, Leonardo S.

doi:10.1109/embc.2016.7590917

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…The parameters used for anisotropic diffusion filtering were set as in Ref. 34. The saturation and brightness thresholding values used to mask SR were set as in Ref.…”

Section: Discussionmentioning

confidence: 99%

Confident texture-based laryngeal tissue classification for early stage diagnosis support

et al. 2017

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Early stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in computer-assisted diagnosis. The objective of this paper is to investigate the use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification. To estimate the classification reliability, a measure of confidence is also exploited. From the endoscopic videos of 33 patients affected by SCC, a well-balanced dataset of 1320 patches, relative to four laryngeal tissue classes, was extracted. With the best performing feature, the achieved median classification recall was 93% [interquartile range [Formula: see text]]. When excluding low-confidence patches, the achieved median recall was increased to 98% ([Formula: see text]), proving the high reliability of the proposed approach. This research represents an important advancement in the state-of-the-art computer-assisted laryngeal diagnosis, and the results are a promising step toward a helpful endoscope-integrated processing system to support early stage diagnosis.

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“…The parameters used for anisotropic diffusion filtering were set as in Ref. 34. The saturation and brightness thresholding values used to mask SR were set as in Ref.…”

Section: Discussionmentioning

confidence: 99%

Confident texture-based laryngeal tissue classification for early stage diagnosis support

et al. 2017

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“…The study by Arahujo et al [20] used the first publicly available database of images of laryngeal lesions provided by Moccia et al [41]. The Moccia et al study [26] obtained an accuracy of 93%, while the same classification using a different methodology in the study by Arahujo et al [20] obtained an accuracy of 98%.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in laryngeal endoscopy: Systematic Review and Meta-Analysis

Žůrek

Rzepakowska

Jasak

et al. 2022

Preprint

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Background: Early and proper diagnosis of laryngeal lesions is necessary to begin treatment of the patient as soon as possible with the possibility of preserve organ functions. Imaging examinations are oft aided by artificial intelligence (AI) to improve quality and facilitate appropriate diagnosis. The aim of the study is to investigate of the diagnostic utility of AI in laryngeal endoscopy. Methods: Five electronic databases (PubMed, Embase, Cochrane, Scopus, Web of Science) were searched for studies published before October 15, 2021 implementing artificial intelligence (AI) enhanced models assessing images of laryngeal lesions taken during laryngeal endoscopy. Outcomes were analyzed in terms of accuracy, sensitivity and specificity. Results: All 13 included studies presented overall low risk of bias. The overall accuracy of AI models was very high (from 0.806 to 0.997) and the number of images used to build and evaluate the models ranged from 120 to 24,667. The accuracy was significantly higher in studies using larger database. The pooled sensitivity and specificity for identification of healthy laryngeal tissue (8 studies) was 0.91 (95% CI: 0.83-0.98) and 0.97 (95% CI: 0.96-0.99), respectively. The same values for differentiation between benign and malignant lesions (7 studies) were 0.91 (95% CI: 0.86-0.96) and 0.95 (95% CI: 0.90-0.99), respectively. The analysis was extended to a comparison of sensitivity and specificity of AI models assessing Narrow Band Imaging (3 studies) and white light endoscopy images (4 studies). The results were similar for both methods, no subgroup effect was revealed (p = 0.406 for sensitivity and p = 0.817 for specificity). Conclusions: In assessing images of laryngeal lesions, AI demonstrates extraordinarily high accuracy, sensitivity, and specificity. AI enhanced diagnostic tools should be introduced into everyday clinical work. The performance of AI diagnoses increases efficacy with the size of the image database when using similar standards for evaluating images. The multicentre cooperation should concentrate on creation of huge database of laryngeal lesions images and implement their sharing, which allows building AI modes with the best performance, based on vast amount of images for learning and testing.

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“…Anatomical structures, as well as surgical tools, can be automatically identified (segmented) or tracked over time to provide surgeons with decision support and context awareness. Exemplary applications include vertebrae [201] segmentation on fluoroscopy images, tissues and surgical tools tracking [202] in 3D US, vessel segmentation [203], organ segmentation and tumor margin assessment in laparoscopic imaging [204,205,206], surgical tool detection in video la- paroscopy [207], cancerous tissue [208] and organs at risk [209,210,211], panorama stitching to enlarge the field of view [212], surface reconstruction in plastic surgery [213], identification in planning radiotherapy CT, brachitherapy [214] and biopsy [81] needles segmentation in iMRI, and pyramidal tract reconstruction [215]. -Physiological parameter estimation: medical images have been used also to esteem some physical and physiological parameters not directly measurable.…”

Section: Raman Spectroscopymentioning

confidence: 99%

A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future

et al. 2020

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With the advent of Minimally Invasive Surgery (MIS), intra-operative imaging has become crucial for surgery and therapy guidance, allowing to partially compensate for the lack of information typical of MIS. This paper reviews the advancements in both classical (i.e. ultrasounds, X-ray, optical coherence tomography and magnetic resonance imaging) and more recent (i.e. multispectral, photoacoustic and Raman imaging) intra-operative imaging modalities. Methods Each imaging modality was analyzed, focusing on benefits and disadvantages in terms of compatibility with the operating room, costs, acquisition time and image characteristics. Tables are included to summarize this information. New generation of hybrid surgical room and algorithms for real time/in room image processing were also investigated. Results Each imaging modality has its own (site-and procedure-specific) peculiarities in terms of spatial and temporal resolution, field of view and contrasted tissues. Besides the benefits that each technique offers for guidance, considerations about operators and patient risk, costs, and extra time required for surgical procedures have to be considered. The current trend is to equip surgical rooms with multimodal imaging systems, so as to integrate multiple information for real-time data extraction and computer-assisted processing. Conclusions The future of surgery is to enhance surgeons eye to minimize

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Automatic workflow for narrow-band laryngeal video stitching

Cited by 10 publications

References 16 publications

Confident texture-based laryngeal tissue classification for early stage diagnosis support

Confident texture-based laryngeal tissue classification for early stage diagnosis support

Artificial Intelligence in laryngeal endoscopy: Systematic Review and Meta-Analysis

A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future

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