State-of-the-art medical image registration methods
Summary paper: TMI
| First Author | Method | Training | Testing | Paper | Code |
|---|---|---|---|---|---|
| Luca Canalini | Registration of ultrasound volumes based on Euclidean distance transform | 1.27 (0.44) (27 cases) | - | arXiv | MeVisLab |
| Wolfgang Wein | Brain-Shift Correction with Image-Based Registration and Landmark Accuracy Evaluation | 1.75 (0.62) | 1.57 (0.96) | LNCS | - |
| Mattias P. Heinrich | Intra-operative Ultrasound to MRI Fusion with a Public Multimodal Discrete Registration Tool | 1.67 (0.54) | 1.87 (0.93) | LNCS | DeedsSSC |
| Inês Machado | Deformable MRI-Ultrasound Registration via Attribute Matching and Mutual-Saliency Weighting for Image-Guided Neurosurgery | 3.35 (1.39) | 2.18 (1.23) | LNCS | cDRAMMS |
| David Drobny | Registration of MRI and iUS Data to Compensate Brain Shift Using a Symmetric Block-Matching Based Approach | 2.90 (3.59) | 3.21 (3.57) | LNCS | NiftyReg |
Values indicates the average (std) distances between larnmark pairs
| First Author | Method | mTREs | Paper | Code |
|---|---|---|---|---|
| David Drobny | Landmark-Based Evaluation of a Block-Matching Registration Framework on the RESECT Pre- and Intra-operative Brain Image Data Set | 1.92 (1.04) | LNCS | NiftyReg |
mTREs: mean target registration errors