Selected projects
Learning-based simulations and rendering for orthodontic treatments
I coordinate the ETH Zurich part of an industry-academy collaboration with Align Technology and the University of Geneva. The goal of this research project is the learning-based simulation and the high-quality rendering of soft-tissue facial changes due to orthodontic treatments. This will allow the patients to previsualize the changes in their faces due to the treatment, contributing to facilitated patient-doctor communication, more accurate treatment outcomes, and increased patient satisfaction. We employ highly heterogeneous medical data consisting of computed tomography (CT), intraoral and facial scan modalities and develop a digital model of the patient, which is then used for simulation of the treatment and photorealistic rendering.
Automated and learning-based plate computation for cleft lip and palate
Cleft lip and palate is a birth defect with a 1 in 700 frequency, affecting approximately 200,000 infants worldwide every year. As part of the BRCCH-funded multidisciplinary project Burden-Reduced Cleft Lip and Palate Care and Healing, of which the technical part I coordinate at ETH Zurich supervising a PhD student and several bachelor students, I conceived and led the development of a fully automated and digital method for computing presurgical plates for infants with cleft lip and palate. The plates facilitate the feeding of the infants and provide a closure of the cleft over the course of a few months, which in return benefits the surgical outcome. The traditional method of generating the plates with silicon impression is cumbersome, requires a dental technician, and puts the airway of infants at risk. Our method requires only an intraoral scan of the infant, and for the training of the machine learning algorithms, it employs a large 3D intraoral scan dataset provided by clinical partners in Switzerland, Poland, and India. The automatically computed plates are then fabricated using 3D printing. Our solution and its graphical user interface (GUI) became part of the clinical routine at the University Hospital of Basel. It has been used for the treatment of more than 30 patients in Switzerland and India (Saveetha Medical Hospital, Chennai, and Bhagwan Mahaveer Jain Hospital, Bangalore). The successful clinical translation of our work was recently awarded by Best Bench-to-Bedside Award at IPCAI Conference in Munich in June 2023.
Smartphone-based low-cost 3D intraoral scanning
A related project I am supervising is in the field of computer vision and artificial intelligence, and its goal is to generate 3D intraoral scans of infants based on smartphone camera photos and videos. Since intraoral scanners are expensive and smartphones are endowed with increasingly more accurate sensors, this could allow patients in low-income countries to be scanned cost-effectively. This can then be used for dental treatments or presurgical plate computation for cleft lip and palate. So far, we have attained submillimeter accuracy in 3D reconstruction in this challenging part of the body, and it has also been tested in a surgical setting at the University Hospital of Basel. For the presurgical plates, we have previously used intraoral scans as input, but in July 2023, three patients in Hyderabad, India, started treatment with automatically computed plates generated based on smartphone-based 3D palatal reconstruction.
Learning-based 3D infant face and head model for medical applications
The existing facial models are almost exclusively generated from adult datasets, rendering their use impossible on applications with infants since infants have different facial and head shapes. In this collaboration with Prof. Andreas Müller’s Research Group Facial & Cranial Anomalies at the University of Basel, we develop face and head models specifically for infants. These models will be used for the treatments of common craniofacial malformations of children. We aim at patient modelling, visualization, and simulations for the treatment of these malformations.
Data-driven sampling optimization for MRI scans
Magnetic resonance imaging (MRI) scans are costly. Moreover, for claustrophobic people, children, and patients who cannot hold their breaths, the long scanning times constitute a challenge. Therefore, it’s high of interest to enable faster scans. When I started my PhD, there were already image processing techniques to reconstruct MRI scans from less Fourier samples instead of the full sampling. However, I had observed back then that there was no systematic method of choosing the sampling schemes, i.e., which samples to take during the scan, except for heuristical solutions. Therefore, I proposed this problem as the focus of PhD thesis proposal and developed a learning-based sampling optimization for MRI. My research used training data acquired from clinical partners such as Lausanne University Hospital (CHUV). Our sampling optimization algorithms allowed higher-quality image reconstructions tailored to the anatomy, the preferred image quality metric, and classical or deep learning-based reconstruction algorithms for a given scan acceleration factor. Our publication in IEEE Transactions on Medical Imaging and the follow-up work addressing the multi-coil and dynamic MRI settings triggered a substantial interest among the medical imaging community in the topic; many research groups followed up on the idea, investigating the joint optimization of sampling and reconstruction algorithms or reinforcement learning for online sampling decisions.
Patents
Gaspard Zoss, Baran Gözcü, Barbara Solenthaler, Linghchen Yang, and Byungsoo Kim, “Data-driven Pyhsics-based Models with Implicit Actuations”, US Patent Application, No. 18/159,651, filed January 25th, 2023.
Baran Gözcü, Barbara Solenthaler, Markus Gross, Till Schnabel, Lasse Lingens, Andreas Müller, Benito Benitez, Prasad Nalabothu et. al., “Automated Methods for Designing and Fabricating an Intra-oral Appliance”, EP 22173641.6, filed May 16, 2022.
Volkan Cevher, Yen-Huan Li, Ilija Bogunovic, Luca Baldassarre, Jonathan Scarlett, and Baran Gözcü “Learning-Based Subsampling”, US 20170109650 A1, filed October 19, 2015, and issued September 25, 2018.
Publications
Lasse Lingens, Baran Gözcü, Till Schnabel, Yoriko Lill, Benito K. Benitez, Prasad Nalabothu, Andreas A. Mueller, Markus Gross, and Barbara Solenthaler, “Image-based 3D reconstruction of cleft lip and palate using a learned shape prior ”, accepted at Applications of Medical AI workshop at MICCAI, Vancouver, Canada, October 2023.
Till N. Schnabel, Baran Gözcü , Paulo Gotardo, Lasse Lingens, Daniel Dorda, Frawa Vetterli, Ashraf Emhemmed, Prasad Nalabothu, Yoriko Lill, Benito K. Benitez, Andreas A. Mueller, Markus Gross and Barbara Solenthaler, “Automated and Data-Driven Plate Computation for Presurgical Cleft Lip and Palate ”, International Conference on Information Processing in Computer-Assisted Interventions (IPCAI) , Munich, Germany, June 2023.
Lingchen Yang, Byungsoo Kim, Gaspard Zoss, Barbara Gözcü, Markus Gross and Barbara Solenthaler, “Implicit Neural Representation for Physics-driven Actuated Soft Bodies ”, Proceedings of ACM SIGGRAPH, Vancouver, Canada, August 2022.
Thomas Sanchez, Baran Gözcü, Ruud B. van Heeswijk, Armin Eftekhari, Efe Ilıcak, Tolga Çukur, and Volkan Cevher, “Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, May 2020.
Baran Gözcü, Thomas Sanchez, and Volkan Cevher, “Rethinking Sampling in Parallel MRI: A Data-Driven Approach”, European Signal Processing Conference (EUSIPCO), A Coruna, Spain, Barcelona, Spain, September 2019.
Baran Gözcü, Rabeeh Karimi Mahabadi, Yen-Huan Li, Efe Ilıcak, Tolga Çukur, Jonathan Scarlett, and Volkan Cevher, “Learning-based Compressive MRI”, IEEE transactions on medical imaging, vol. 37, no. 6, pp. 1394-1406, June 2018.
Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett, Baran Gözcü, Ilija Bogunovic, and Volkan Cevher, “Learning-Based Compressive Subsampling”, IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 4, pp. 809-822, March 2016.
Baran Gözcü, Luca Baldassarre, Quoc Tran-Dinh, Cosimo Aprile, and Volkan Cevher, “A Primal-dual Framework for Mixtures of Regularizers”, European Signal Processing Conference (EUSIPCO), Nice, France, September 2015.
Bubacarr Bah, Stephen Becker, Volkan Cevher, and Baran Gözcü, “Metric Learning with Rank and Sparsity Constraints”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, May 2014.
Baran Gözcü, Afsaneh Asaei, and Volkan Cevher, “Manifold Sparse Beamforming”, IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Saint Martin, French West Indies, France, December 2013.