Computed tomography data collection of the complete human mandible and valid clinical ground truth models

TL;DR

This paper presents a unique, validated CT dataset of the complete human mandible with expert-annotated ground truth models, addressing the scarcity of reliable medical datasets for algorithm evaluation.

Contribution

It provides a comprehensive, artifact-free CT dataset with validated ground truth segmentations, enabling improved algorithm testing and development for mandibular imaging.

Findings

20 validated ground truth models from 10 CT scans

Models created independently by clinical experts

Statistical validation confirms model accuracy

Abstract

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.