Automatic Workflow for Narrow-Band Laryngeal Video Stitching

In narrow band (NB) laryngeal endoscopy, the clinician usually positions the endoscope near the tissue for a correct inspection of possible vascular pattern alterations, indicative of laryngeal malignancies. The video is usually reviewed many times to refine the diagnosis, resulting in loss of time since the salient frames of the video are mixed with blurred, noisy, and redundant frames caused by the endoscope movements. The aim of this work is to provide to the clinician a unique larynx panorama, obtained through an automatic frame selection strategy to discard non-informative frames. Anisotropic diffusion filtering was exploited to lower the noise level while encouraging the selection of meaningful image features, and a feature-based stitching approach was carried out to generate the panorama. The frame selection strategy, tested on on six pathological NB endoscopic videos, was compared with standard strategies, as uniform and random sampling, showing higher performance of the subsequent stitching procedure, both visually, in terms of vascular structure preservation, and numerically, through a blur estimation metric

NBI-InfFrames

<p>The <strong>NBI-InfFrames </strong>dataset<strong> </strong>aims to provide the surgical data science community with a labeled dataset for the identification of informative endoscopic video frames. </p> <p>It is composed of 720 video frames. The frames are manually extracted and labeled from 18 narrow-band laryngoscopic videos of 18 different patients affected by laryngeal spinocellular carcinoma (diagnosed after histopathological examination). </p> <p>The frames include 180 informative (<strong>I</strong>) video frames, 180 blurred (<strong>B</strong>) frames, 180 frames with saliva or specular reflections (<strong>S</strong>) and 180 underexposed (<strong>U</strong>) frames.</p> <p>The dataset was created for testing the method proposed in S. Moccia, et al. "<em>Learning-based classification of informative laryngoscopic frames.</em>" COMPUTER METHODS AND PROGRAM IN BIOMEDICINE, (accepted for publication).</p> <p>The folder<em> <strong>FRAMES.zip</strong> </em>contains 3 subfolders (FOLD1, FOLD2, FOLD3), which are the 3 folds used for cross-validation purpose in the frame classification performance assessment. Data separation in the folds is performed both at image- and patient-level.</p> <p>Each subfolder contains 4 folders relative to the four frame classes, i.e., <strong>I</strong>, <strong>B</strong>, <strong>S</strong> and <strong>U</strong>.</p