Tooth Semantic Segmentation Dataset
The progress of video and photo editing applications relies heavily on high-quality datasets for machine learning models training. Our carefully selected datasets play a crucial role in enhancing the abilities of such applications, by offering accurate segmentation and recognition of different elements within images and videos.
The "Tooth Semantic Segmentation Dataset" is tailored for the healthcare sector, comprising a collection of internet-collected images with a resolution of 256 x 256 pixels. This dataset is dedicated to semantic segmentation, focusing on labeling different parts of the teeth, including the lower row, incisors, and the upper row, from various angles to provide detailed dental imagery for analysis and educational purposes.
Sample
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Specification
Dataset ID
MD-Image-083
Dataset Name
Tooth Semantic Segmentation Dataset
Data Type
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Volume
About 2k
Data Collection
Internet collected images. Resolution is 256 x256.
Annotation
Semantic Segmentation
Annotation Notes
Collecting images of teeth from different angles, label the lower row of teeth, incisors and the rest of the upper row of teeth pictures with semantic segmentation .
Application Scenarios
Healthcare
Data Collections
This dataset includes approximately 2k images, each segmented to delineate different parts of the teeth structure, such as the lower row, incisors, and the rest of the upper row. The semantic segmentation of these dental images offers valuable data for a wide range of applications in healthcare, from dental education and training to the development of dental imaging software and diagnostic tools, enhancing the precision and effectiveness of dental care and treatment planning.
Quality Assurance
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Relevant Open Datasets
To supplement our Face Parsing Dataset, users can explore these open datasets for additional resources:
FASSEG Repository [Learn more]
This collection offers datasets for frontal face segmentation (Frontal01 and Frontal02) and a dataset for faces in multiple poses (Multipose01).These datasets can be valuable for training models to perform face segmentation in different orientations and conditions.
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