基于深度卷积LSTM网络的喉部高速视频中声门与声带全自动分割数据集

资源简介

基于深度卷积LSTM网络的喉部高速视频中声门与声带全自动分割数据集

【数据集背景】

本数据集源站标题为《Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network》。配套论文《Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network》发表于 PLOS ONE（2020），DOI：10.1371/journal.pone.0227791。源站首次发布：2020-02-10。

【数据内容】

源站原始描述：The objective investigation of the dynamic properties of vocal fold vibrations demands the recording and further quantitative analysis of laryngeal high-speed video (HSV). Quantification of the vocal fold vibration patterns requires as a first step the segmentation of the glottal area within each video frame from which the vibrating edges of the vocal folds are usually derived. Consequently, the outcome of any further vibration analysis depends on the quality of this initial segmentation process…

【数据结构与技术规格】

文件数 2 个，合计 28.40 MB。数据集 DOI：10.1371/journal.pone.0227791。

【主题与分类】

源站学科分类：Space Science、Medicine、Neuroscience、Physiology、Sociology、Biological Sciences not elsewhere classified。主题标签：Long Short-Term Memory、vibration、HSV、Convolutional LSTM Network、Convolutional Neural Network、approach、segmentation、laryngeal、landmarks amounts 2.2 pixels、DC。

【适用方向】

源站学科分类标注为 Space Science、Medicine、Neuroscience、Physiology、Sociology、Biological Sciences not elsewhere classified。

【获取与许可】

源站页面：https://plos.figshare.com/articles/dataset/Fully_automatic_segmentation_of_glottis_and_vocal_folds_in_endoscopic_laryngeal_high-speed_videos_using_a_deep_Convolutional_LSTM_Network/11831904

使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。

规范引用：Fehling, Mona Kirstin; Grosch, Fabian; Schuster, Maria Elke; Schick, Bernhard; Lohscheller, Jörg (2020). Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network. PLOS ONE. Dataset

【使用注意】

· 源站最后修改于 2023-06-01，引用时请注意版本时效。