CMF defects database: A craniomaxillofacial defects dataset and a data-driven repair method

Abstract

Due to the large number of medical images generated each year, data-driven machine learning methods hold great promise in the field of medical image diagnosis and analysis. In recent years, the application of deep neural networks in oral and cranio-maxillofacial surgery has gradually increased, helping surgeons to obtain more comprehensive information and develop more accurate treatment planning, including segmentation and recognition of anatomical structures, disease diagnosis, and automatic surgical planning. This paper presents a new dataset of maxillofacial defect images, containing computed tomography (CT) data of 125 normal human cranial skeletal 3D reconstructions and 2625 corresponding craniomaxillofacial bone defects. The data are classified according to seven different types of defects which are anterior skull and forehead, zygomatic arch, zygomatic bone, Naso-orbital-ethmoidal (NOE), wall of the maxillary sinus, mandibular body, mandibular ramus, and condylar respectively. Each kind of defect is further divided into three categories according to the different locations, including left side, right side, and bilateral sides (or defect passes through the midline of the face). There are three main contributions: first, a dataset is publicly available to assist researchers in the development of data-based methods for generating missing portions of the skull; second, a repair method is proposed to evaluate the dataset; third, a review of various related datasets. The release of this dataset aims to advance artificial intelligence methods in the field of oral and cranio-maxillofacial surgery and plastic surgery helping physicians to reduce the difficulty and time consumption while improving the efficiency and accuracy of prosthesis design.