TY - JOUR
T1 - OII-DS
T2 - A benchmark Oral Implant Image Dataset for object detection and image classification evaluation
AU - Nie, Qianqing
AU - Li, Chen
AU - Yang, Jinzhu
AU - Yao, Yudong
AU - Sun, Hongzan
AU - Jiang, Tao
AU - Grzegorzek, Marcin
AU - Chen, Ao
AU - Chen, Haoyuan
AU - Hu, Weiming
AU - Li, Rui
AU - Zhang, Jiawei
AU - Wang, Danning
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/12
Y1 - 2023/12
N2 - In recent years, there is been a growing reliance on image analysis methods to bolster dentistry practices, such as image classification, segmentation and object detection. However, the availability of related benchmark datasets remains limited. Hence, we spent six years to prepare and test a bench Oral Implant Image Dataset (OII-DS) to support the work in this research domain. OII-DS is a benchmark oral image dataset consisting of 3834 oral CT imaging images and 15240 oral implant images. It serves the purpose of object detection and image classification. To demonstrate the validity of the OII-DS, for each function, the most representative algorithms and metrics are selected for testing and evaluation. For object detection, five object detection algorithms are adopted to test and four evaluation criteria are used to assess the detection of each of the five objects. Additionally, mean average precision serves as the evaluation metric for multi-objective detection. For image classification, 13 classifiers are used for testing and evaluating each of the five categories by meeting four evaluation criteria. Experimental results affirm the high quality of our data in OII-DS, rendering it suitable for evaluating object detection and image classification methods. Furthermore, OII-DS is openly available at the URL for non-commercial purpose: https://doi.org/10.6084/m9.figshare.22608790.
AB - In recent years, there is been a growing reliance on image analysis methods to bolster dentistry practices, such as image classification, segmentation and object detection. However, the availability of related benchmark datasets remains limited. Hence, we spent six years to prepare and test a bench Oral Implant Image Dataset (OII-DS) to support the work in this research domain. OII-DS is a benchmark oral image dataset consisting of 3834 oral CT imaging images and 15240 oral implant images. It serves the purpose of object detection and image classification. To demonstrate the validity of the OII-DS, for each function, the most representative algorithms and metrics are selected for testing and evaluation. For object detection, five object detection algorithms are adopted to test and four evaluation criteria are used to assess the detection of each of the five objects. Additionally, mean average precision serves as the evaluation metric for multi-objective detection. For image classification, 13 classifiers are used for testing and evaluating each of the five categories by meeting four evaluation criteria. Experimental results affirm the high quality of our data in OII-DS, rendering it suitable for evaluating object detection and image classification methods. Furthermore, OII-DS is openly available at the URL for non-commercial purpose: https://doi.org/10.6084/m9.figshare.22608790.
KW - Image classification
KW - Image dataset
KW - Object detection
KW - Oral implant
KW - Oral surface CT
UR - http://www.scopus.com/inward/record.url?scp=85175529681&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85175529681&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2023.107620
DO - 10.1016/j.compbiomed.2023.107620
M3 - Article
C2 - 37922604
AN - SCOPUS:85175529681
SN - 0010-4825
VL - 167
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 107620
ER -