1. Dynamic programming and automated segmentation of optical coherence tomography images of the neonatal subglottis: enabling efficient diagnostics to manage subglottic stenosis
- Author
-
Kozlowski, Konrad M, Sharma, Giriraj K, Chen, Jason J, Qi, Li, Osann, Kathryn, Jing, Joseph C, Ahuja, Gurpreet S, Heidari, Andrew E, Chung, Phil-Sang, Kim, Sehwan, Chen, Zhongping, and Wong, Brian J-F
- Subjects
Paediatrics ,Biomedical and Clinical Sciences ,Pediatric ,Preterm ,Low Birth Weight and Health of the Newborn ,Bioengineering ,Perinatal Period - Conditions Originating in Perinatal Period ,Infant Mortality ,Biomedical Imaging ,Clinical Research ,Detection ,screening and diagnosis ,4.2 Evaluation of markers and technologies ,4.1 Discovery and preclinical testing of markers and technologies ,Algorithms ,Humans ,Image Interpretation ,Computer-Assisted ,Infant ,Newborn ,Laryngostenosis ,Larynx ,Tomography ,Optical Coherence ,diagnostic imaging ,intubation injury ,neonate ,optical coherence tomography ,subglottic stenosis ,texture analysis ,Optical Physics ,Biomedical Engineering ,Opthalmology and Optometry ,Optics ,Ophthalmology and optometry ,Biomedical engineering ,Atomic ,molecular and optical physics - Abstract
Subglottic stenosis (SGS) is a challenging disease to diagnose in neonates. Long-range optical coherence tomography (OCT) is an optical imaging modality that has been described to image the subglottis in intubated neonates. A major challenge associated with OCT imaging is the lack of an automated method for image analysis and micrometry of large volumes of data that are acquired with each airway scan (1 to 2 Gb). We developed a tissue segmentation algorithm that identifies, measures, and conducts image analysis on tissue layers within the mucosa and submucosa and compared these automated tissue measurements with manual tracings. We noted small but statistically significant differences in thickness measurements of the mucosa and submucosa layers in the larynx (p
- Published
- 2019