Recently, digital dentistry is evolving rapidly along with the rapid innovation of artificial intelligence and the development of CBCT, intra-oral and face scanner, and dental 3D printing skills. Digital dentistry can improve efficiency of streamlining the dentist's workflow and provides more accurate orthodontic diagnosis, treatment planning, and surgical guides.  One of the fundamental components of digital dentistry is 3D segmentation of teeth, jaws, and skulls from CBCT images, because accurate digital models of the individual tooth geometry and jaws are important for simulating prosthetic evaluation, cephalometric analysis, computer-aided digital implant planning, predicting bite irregularities, and so on.

    Automatic and accurate segmentation of individual teeth from 3D CBCT images is a challenging task due to contact with adjacent teeth and similar intensities between tooth and their neighboring alveolar bone.

Obstetricians mainly use ultrasound imaging for fetal biometric measurement. However, such measurement is cumbersome. Hence, there is urgent need for automatic biometric estimation. Automated analysis of ultrasound images is complicated owing to the patient-specific, operator-dependent, and machine-specific characteristics of such images. We developed methods for the automatic fetal biometry estimation from 2D ultrasound data through several processes consisting of a specially designed  neural networks.