用于外科手术技能训练的3D喉部模型的开发与验证
包含3D打印喉部模型及病理声带图像,用于内窥镜显微喉手术训练评估。

基本信息
资源简介
用于外科手术技能训练的3D喉部模型的开发与验证
【数据集背景】
本数据集源站标题为《Development and validation of a 3D laryngeal model in surgical skills training》。配套论文《Development and validation of a 3D laryngeal model in surgical skills training》发表于 Brazilian Journal of Otorhinolaryngology(2023),DOI:10.1016/j.bjorl.2021.09.010。源站首次发布:2023-02-18。
【数据内容】
源站原始描述:Abstract Objective: To present and validate a new simulation model for endoscopic phonomicrosurgery training as an improved teaching method. Methods: A low-cost artificial model was assembled using 3D printing, silicone, and gelatin. The study was designed to test the model’s ability to carry out training and teaching of endoscopic phonomicrosurgery. The synthetic model was built to simulate normal and pathological vocal folds such as polyps, intracordal cysts and keratoses, made of silicone and…
【数据结构与技术规格】
文件数 15 个,合计 6.19 MB。数据集 DOI:10.6084/m9.figshare.22121190.v1。
【主题与分类】
源站学科分类:Otorhinolaryngology。主题标签:Anatomical models、Endoscopy、Laryngology、Vocal folds、Microsurgery。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面:https://scielo.figshare.com/articles/dataset/Development_and_validation_of_a_3D_laryngeal_model_in_surgical_skills_training/22121190
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Saliba, Thiago Victal; Barros, Rui Sérgio Monteiro de (2023). Development and validation of a 3D laryngeal model in surgical skills training. SciELO journals. Dataset
【使用注意】
· 源站最后修改于 2023-06-13,引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取(Figshare)
- 源站标题:Development and validation of a 3D laryngeal model in surgical skills training
- DOI:10.6084/m9.figshare.22121190.v1
- 发布日期:2023-02-18
- 文件数:15 个
- 文件总体积:6.19 MB
- 源站页面:https://scielo.figshare.com/articles/dataset/Development_and_validation_of_a_3D_laryngeal_model_in_surgical_skills_training/22121190
命令行下载
curl -L -o "bb1aea22b2e352e9abde0bb928058ff6e0364c43.tif" "https://ndownloader.figshare.com/files/39311673"
curl -L -o "8456b5b06c5389b228fe5db0de0eea1ca7dc0042.png" "https://ndownloader.figshare.com/files/39311676"
curl -L -o "51894c04d9d4ebebd726f3f161b3922fed30ea4a.jpg" "https://ndownloader.figshare.com/files/39311679"
curl -L -o "13a94d8e79ea8baea7362d8c6ee57d71f928f9ae.tif" "https://ndownloader.figshare.com/files/39311682"
curl -L -o "6448adac7ffdeb54209d3bc36e48a35e5a0c9f7f.png" "https://ndownloader.figshare.com/files/39311685"
curl -L -o "d72d58eb50bd1a059e40a1ba62dfcd08fde162a5.jpg" "https://ndownloader.figshare.com/files/39311688"
curl -L -o "04ac241e6f28b08757871c7b86c7d2062509bc67.tif" "https://ndownloader.figshare.com/files/39311691"
curl -L -o "ed70be350e9a912c2155fe1d9ebcb97211f69ea5.png" "https://ndownloader.figshare.com/files/39311694"
文件清单
bb1aea22b2e352e9abde0bb928058ff6e0364c43.tif(774.33 KB)8456b5b06c5389b228fe5db0de0eea1ca7dc0042.png(314.79 KB)51894c04d9d4ebebd726f3f161b3922fed30ea4a.jpg(6.17 KB)13a94d8e79ea8baea7362d8c6ee57d71f928f9ae.tif(777.59 KB)6448adac7ffdeb54209d3bc36e48a35e5a0c9f7f.png(329.94 KB)d72d58eb50bd1a059e40a1ba62dfcd08fde162a5.jpg(6.29 KB)04ac241e6f28b08757871c7b86c7d2062509bc67.tif(923.33 KB)ed70be350e9a912c2155fe1d9ebcb97211f69ea5.png(347.94 KB)efdaa46958f16810d81f38da1281b36717738f52.jpg(3.16 KB)47fb9b893cce6589cb60e0133d82c59d18e3ddf3.tif(895.68 KB)27f0d533f949508e832713082f83d443c91fa40f.png(376.11 KB)74c4ab228fa7514e0df590a961fdeb6df146d2b6.jpg(5.28 KB)b40258fac9a4570814ee6ebb549b37bed9c0dd55.tif(1.23 MB)8f0ffed163bba33690862e3c990e27c888ad9956.png(200.61 KB)5a079d1f1e23c9d5f10803a35c235d4be68e87cc.jpg(8.05 KB)
文件总体积 6.19 MB(源站 API 实测)。
数据加载示例(图像类)
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
数据说明
Abstract Objective: To present and validate a new simulation model for endoscopic phonomicrosurgery training as an improved teaching method. Methods: A low-cost artificial model was assembled using 3D printing, silicone, and gelatin. The study was designed to test the model’s ability to carry out training and teaching of endoscopic phonomicrosurgery. The synthetic model was built to simulate normal and pathological vocal folds such as polyps, intracordal cysts and keratoses, made of silicone and gelatin and embedded in a larynx framework made by 3D printing. Simulations of endoscopic surgical procedures were performed and documented through photographs and videos and the images were submitted to the evaluation of a group of 17 otorhinolaryngologists who used a Likert scale questionnaire. The responses were submitted to an agreement analysis using the sum of the scores obtained for the responses as an appropriate level of validation. Cronbach’s alpha index was calculated to measure the degree of the questionnaire internal consistency. Result: The evaluations indicated maximum approval for the model adequacy for use in practical classes and for the teaching of the procedures, as well as in the overall satisfaction with the model in the use of surgical training. Conclusion: The present proposal for training laryngeal endoscopic surgery in a 3D synthetic model is a viable option according to the validation methodology used in the present study.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.22121190.v1(source: https://scielo.figshare.com/articles/dataset/Development_and_validation_of_a_3D_laryngeal_model_in_surgical_skills_training/22121190)。
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