20 citations · 34 across the 11 of their papers we have counts for
13 papers
LUMINR: Intensity-conditioned Meta-learned INRs for Accelerated Medical Image Registration
Damian Rynczak, Marek Wodzinski
Implicit neural representations (INRs) have been applied to deformable image registration, where a lightweight network maps a spatial coordinate to a deformation vector. Because an…
MedPCFM-TED: One-Step Point Cloud Flow Matching for Implant Generation via Teacher-Guided Endpoint Distillation
Kamil Kwarciak, Marek Wodzinski
Cranial implant generation is an important task in medical imaging. Recent point cloud based generative methods, particularly flow matching, offer strong reconstruction quality and…
MAMA-FLUX.2: Image-to-Image Synthesis of Post-Contrast Breast DCE-MRI for the MAMA-SYNTH Challenge
Kamil Kwarciak, Marek Wodzinski
Dynamic contrast-enhanced breast MRI is central to cancer diagnosis and monitoring, but requires gadolinium-based contrast agents. In this work, we address pre-to-post contrast bre…
Automated segmentation of pediatric neuroblastoma on multi-modal MRI: Results of the SPPIN challenge at MICCAI 2023
M. A. D. Buser, D. C. Simons, M. Fitski +27
Surgery plays an important role within the treatment for neuroblastoma, a common pediatric cancer. This requires careful planning, often via magnetic resonance imaging (MRI)-based…
Automatic Skull Reconstruction by Deep Learnable Symmetry Enforcement
Marek Wodzinski, Mateusz Daniol, Daria Hemmerling
Every year, thousands of people suffer from skull damage and require personalized implants to fill the cranial cavity. Unfortunately, the waiting time for reconstruction surgery ca…
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation
Kamil Kwarciak, Mateusz Daniol, Daria Hemmerling +1
The skull segmentation from CT scans can be seen as an already solved problem. However, in MR this task has a significantly greater complexity due to the presence of soft tissues r…
Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models
Marek Wodzinski, Kamil Kwarciak, Mateusz Daniol +1
Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling…
RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge
Marek Wodzinski, Niccolò Marini, Manfredo Atzori +1
The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from diffe…
Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoders
Marek Wodzinski, Daria Hemmerling, Mateusz Daniol
Thousands of people suffer from cranial injuries every year. They require personalized implants that need to be designed and manufactured before the reconstruction surgery. The man…
Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases
Mateusz Daniol, Daria Hemmerling, Jakub Sikora +3
Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for trackin…
Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects
Kamil Kwarciak, Marek Wodzinski
The design of personalized cranial implants is a challenging and tremendous task that has become a hot topic in terms of process automation with the use of deep learning techniques…
High-Resolution Cranial Defect Reconstruction by Iterative, Low-Resolution, Point Cloud Completion Transformers
Marek Wodzinski, Mateusz Daniol, Daria Hemmerling +1
Each year thousands of people suffer from various types of cranial injuries and require personalized implants whose manual design is expensive and time-consuming. Therefore, an aut…
Deep Learning-based Framework for Automatic Cranial Defect Reconstruction and Implant Modeling
Marek Wodzinski, Mateusz Daniol, Miroslaw Socha +3
The goal of this work is to propose a robust, fast, and fully automatic method for personalized cranial defect reconstruction and implant modeling. We propose a two-step deep learn…