UFBA-UESC深度牙科影像数据集
UFBA-UESC Dental Images Deep 是与颌骨肿瘤和囊肿(Jaw tumors and cysts)相关的公开数据资源,主要包含牙科全景片、牙齿语义/实例分割和牙位编号标注,数据模态为图像,适合用于图像分割、模型训练、基准评测或临床特征分析。

基本信息
资源简介
UFBA-UESC深度牙科影像数据集
【数据集背景】
本数据集源站标题为《IvisionLab/deep-dental-image》。源站首次发布:2019-02-08。
【数据内容】
源站原始描述:Mask R-CNN for instance segmentation of panoramic X-ray images # Deprecation Warning Do not request this data set anymore. REQUESTS WILL BE IGNORED. This data set has been deprecated in favor of a new, soon-to-be-released data set. # Deep instance segmentation of teeth in panoramic X-ray images Mask R-CNN for instance segmentation of panoramic X-ray images # Code Pyhon # Citation JADER, G., FONTINELE J.; RUIZ, M; ABDALLA, K.; PITHON, M.; OLIVEIRA, L. Deep instance segmentation of teeth in panora…
【适用方向】
影像类数据可用于医学图像分析模型(检测、分割、分类)的训练与评测,也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面:https://github.com/IvisionLab/deep-dental-image
使用许可:MIT。
【使用注意】
· 源站最后修改于 2023-01-13,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
git clone https://github.com/IvisionLab/deep-dental-image.git
curl -L -o repo.zip https://github.com/IvisionLab/deep-dental-image/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录(使用方式)
Deprecation Warning
Do not request this data set anymore. REQUESTS WILL BE IGNORED.
This data set has been deprecated in favor of a new, soon-to-be-released data set.
Deep instance segmentation of teeth in panoramic X-ray images
Mask R-CNN for instance segmentation of panoramic X-ray images
Code
Pyhon
Citation
JADER, G., FONTINELE J.; RUIZ, M; ABDALLA, K.; PITHON, M.; OLIVEIRA, L. Deep instance segmentation of teeth
in panoramic X-ray images. In: Conference on Graphics, Patterns and Images (SIBGRAPI), 2018.
<!## Request Dataset
Please send an e-mail to lrebouca@ufba.br to receive a link to the UFBA_UESB_DENTAL_IMAGES_DEEP dataset. Your e-mail must be sent from a valid institutional account, and include the following text (copy and paste the text below, filling the required fields):
"Subject: Request to download UFBA_UESB_DENTAL_IMAGES_DEEP dataset.
- Name: [your first and last name]
- Affiliation: [university where you work]
- Department: [your department]
- Current position: [your job title]
- E-mail: [must be the e-mail at the above mentioned institution]
I have read and agreed to follow the terms and conditions below:
The use of the UFBA_UESB_DENTAL_IMAGES_DEEP dataset is defined by the following conditions:
-
This dataset is provided “AS IS”, without any express or implied warranty. Although every effort has been made to ensure accuracy, IvisionLab does not take any responsibility for errors or omissions;
-
Without the expressed permission of IvisionLab, any of the following will be considered illegal: redistribution, modification, and commercial usage of this dataset in any way or form, either partially or in its entirety;
-
All images in this dataset are only allowed for demonstration in academic publications and presentations;
This dataset will only be used for research purposes. I will not make any part of this dataset available to a third party. I’ll not sell any part of this dataset or make any profit from its use.
[your signature]"
P.S. A link to the dataset file will be sent as soon as possible.>
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。
数据缺失?
依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。




