Segmentation and identification of tooth instances in cone-beam CT scans
The three dimensional labeling and identification of teeth in cone-beam CT scans is a time-consuming and challenging process which is exacerbated by (partially) missing teeth, tooth misalignments as well as the presence of implants or other utilities like metal plates or wires. As part of this project we develop an AI-based instance segmentation algorithm to solve both problems: the accurate labeling and the correct classification of teeth. This can be used, for example, to simplify the planning and preparation of treatments or surgeries.
Other Collaborations
Paving the way for future mineral processing and recycling technologies through large-scale analysis of particulate samples
Understanding and quantifying the exact composition of mineral samples paves the way towards advanced methodologies that not only increase the effectiveness of ore processing but also enable future recycling technologies. To this end, samples consisting of ground particles embedded into an epoxy matrix are imaged with computed tomography (CT) at micrometer resolution. Due to the […]
Understanding and analyzing plant roots using semantic segmentation of MRI images
The optimization of plants has long focused on the above-ground parts. Recently, new efforts are being made to exploit the potential below ground. To this end, our partners at the FZJ have developed an imaging system which enables imaging the root system throughout the growth of the plants using MRI. Besides qualitative analysis, a precise […]