Computed tomography data collection of the complete human mandible and valid clinical ground truth models
This addresses the problem of limited and unreliable ground truth data for medical image segmentation, particularly in maxillomandibular CT studies, though it is incremental as it focuses on a specific anatomical region.
The authors tackled the scarcity of high-quality medical ground truth data for evaluating segmentation algorithms by providing a unique dataset of the complete human mandible, including 20 valid ground truth models from 10 CT scans without artefacts, which were statistically validated by clinical experts.
Image-based algorithmic software segmentation is an increasingly important topic in many medical fields. Algorithmic segmentation is used for medical three-dimensional visualization, diagnosis or treatment support, especially in complex medical cases. However, accessible medical databases are limited, and valid medical ground truth databases for the evaluation of algorithms are rare and usually comprise only a few images. Inaccuracy or invalidity of medical ground truth data and image-based artefacts also limit the creation of such databases, which is especially relevant for CT data sets of the maxillomandibular complex. This contribution provides a unique and accessible data set of the complete mandible, including 20 valid ground truth segmentation models originating from 10 CT scans from clinical practice without artefacts or faulty slices. From each CT scan, two 3D ground truth models were created by clinical experts through independent manual slice-by-slice segmentation, and the models were statistically compared to prove their validity. These data could be used to conduct serial image studies of the human mandible, evaluating segmentation algorithms and developing adequate image tools.