Machine Learning-Based Noise Reduction for Craniofacial Bone Segmentation in CT Images

Abstract

Objective: When high X-ray absorption rate materials such as metal prosthetics are in the field of CT scan, noise called metal artifacts might appear. In reconstructing a three-dimensional bone model from X-ray CT images, the metal artifacts remain. Often, the image of the scanning bed also remains. A machine learning-based system to reduce noises in the craniofacial CT images was constructed. Methods: DICOM images of CT archives of patients with head and neck tumors were used. The metal artifacts and beds were removed from the threshold segmented images to obtain the target bony images. U-nets, respectively with the function loss of mean squared error, Dice and Jaccard, were trained by the datasets consisting of 5671 DICOM images and corresponding target images. DICOM images of 2000 validation datasets were given to the trained models and predicted images were obtained. Results: The use of mean squared errors presented superiority to Dice or Jaccard loss. The mean prediction error pixels were 14.43, 778.57, and 757.60 respectively per 512 x 512 pixeled image. Conclusion: Automatic CT image noise reduction system was constructed. Dedicated to the delineation of craniofacial bones, the presented study showed high prediction accuracy. The “correctness” of the predictions made by this system may not be guaranteed, but the results were generally satisfactory.