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Dr. Marko RakSenior Research ScientistFaculty of Computer ScienceOtto-von-Guericke University Magdeburg Office: G82-161Phone: ++49 391 67-52189 ed.ugvo.sc.gsi@kar :liaM-E |
Research Interests
As a postdoctoral researcher, I am involved with the image analysis projects of our group. My research focuses on improving computer-assisted diagnosis, intervention and therapy tasks using state-of-the-art computer vision, image processing and machine learning techniques.
Studies that took place during my time in the VAR group are listed below. For a complete overview, including work outside of the group, please have a look at my profile on Google Scholar.
Scientific Publications
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| Bashkanov, O., Rak, M., Engelage, L., Hansen, C. (2024) |
| Augmenting Prostate MRI Dataset with Synthetic Volumetric Images from Zone-conditioned Diffusion Generative Model |
| MICCAI Workshop Deep Generative Models (DGM4MICCAI), accepted |
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| Gulamhussene, G., Bashkanov, O., Omari, J., Pech, M., Hansen, C., Rak, M. (2023) |
| Using Training Samples as Transitive Information Bridges in Predicted 4D MRI |
| MICCAI Workshop on Medical Image Learning with noisy and Limited Data (MILLanD), pp. 237-245 |
Paper 
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| Gulamhussene, G., Rak, M., Bashkanov, O., Joeres, F., Omari, J., Pech, M., Hansen, C. (2023) |
| Transfer-Learning is a Key Ingredient to Fast Deep Learning-Based 4D Liver MRI Reconstruction |
| Nature Scientific Reports, 13, pp. 11227 |
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| Bashkanov, O., Rak, M., Engelage, L., Lumiani, A., Muschter, R. Hansen, C. (2023) |
| Automatic Detection of Prostate Cancer Grades and Chronic Prostatitis in Biparametric MRI |
| Journal Computer Methods and Programs in Biomedicine, 239, pp. 107624 |
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| Bashkanov, O., Rak, M., Engelage, L., Hansen, C. (2023) |
| Automatic Patient-level Diagnosis of Prostate Disease with Fused 3D MRI and Tabular Clinical Data |
| Medical Imaging with Deep Learning (MIDL), pp. 128:1–14 |
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| Gulamhussene, G., Meyer, A., Rak, M., Bashkanov, O., Omari, J., Pech, M., Hansen, C. (2022) |
| Predicting 4D Liver MRI for MR-guided Interventions |
| Computerized Medical Imaging and Graphics, 101, pp. 102122 |
Download test data set  |
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| Ernst, P. and Rak, M. and Hansen, C. and Rose, G. and Nuernberger, A. (2021) |
| Trajectory upsampling for sparse conebeam projections using convolutional neural networks |
| Proceedings of the 16th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (FULLY3D), Society of Photo-Optical Instrumentation Engineers (SPIE) |
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| Meyer, A., Mehrtash, A., Rak, M., Bashkanov, O., Langbein, B., Ziaei, A., Kibel, A., Tempany, C., Hansen, C., Tokuda, J. (2021) |
| Domain Adaptation for Segmentation of Critical Structures for Prostate Cancer Therapy |
| Nature Scientific Reports, 11, pp. 11480 |
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| Wei, W., Haishan, X., Alpers, J., Rak, M., Hansen, C. (2021) |
| A Deep Learning Approach for 2D Ultrasound and 3D CT/MR Image Registration in Liver Tumor Ablation |
| Computer Methods and Programs in Biomedicine, 206, pp. 106117 |
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| Gulamhussene, G., Das, A., Spiegel, J., Punzet, D., Rak, M., Hansen, C. (2023) |
| Needle Tip Tracking during CT-guided Interventions using Fuzzy Segmentation |
| German Workshop on Medical Image Computing, pp. 285–291 |
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| Gulamhussene, G., Spiegel, J., Das, A., Rak, M., Hansen, C. (2023) |
| Deep Learning-based Marker-less Pose Estimation of Interventional Tools using Surrogate Keypoints |
| German Workshop on Medical Image Computing, pp. 292–298 |
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| Bashkanov, O., Meyer, A., Schindele, D., Schostak, M., Toennies, K., Hansen, C., Rak, M. (2021) |
| Learning Multi-Modal Volumetric Prostate Registration with Weak Inter-Subject Spatial Correspondence |
| Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), Nice, France, pp. 1817-1821 |
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| Gulamhussene, G., Joeres, F., Rak, M., Pech, M., Hansen, C. (2020) |
| 4D MRI: Robust Sorting of Free Breathing MRI Slices for use in Interventional Settings |
| PLOS ONE, 15(6), pp. e0235175 |
| Download test data set |
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| Wei, W., Rak, M., Alpers, J., Hansen, C. (2020) |
| Towards Fully Automatic 2D US to 3D CT/MR Registration: A novel Segmentation-Based Strategy |
| Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), Iowa City, Iowa, USA, in print |
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| Ernst, P., Hille, G., Hansen, C., Toennies, K., Rak, M. (2019) |
| A CNN-Based Framework for Statistical Assessment of Spinal Shape and Curvature in Whole-Body MRI Images of Large Populations |
| Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen, China, pp. 3-11 |
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| Rak, M., Steffen, J., Meyer, A., Hansen, C., Toennies, K. (2019) |
| Combining Convolutional Neural Networks and Star Convex Cuts for Fast Whole Spine Vertebra Segmentation in MRI |
| Computer Methods and Programs in Biomedicine, 177, pp. 47-56 |
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| Meyer, A., Rak, M., Schindele, D., Blaschke, S., Schostak, M., Fedorov, A., Hansen, C. (2019) |
| Towards Patient-Individual PI-RADS v2 Sector Map: CNN for Automatic Segmentation of Prostatic Zones from T2-Weighted MRI |
| Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI 2019) , Venice, Italy , 696-700 |
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| Wei, W., Xu, H., Alpers, J., Tinabao, Z., Wang, L., Rak, M., Hansen, C. (2019) |
| Fast Registration for Liver Motion Compensation in Ultrasound-guided Navigation |
| Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI 2019) , Venice, Italy , pp. 1132-1136 |
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| Meyer, A., Ghosh, S., Schindele, D., Schostak, M., Stober, S., Hansen, C., Rak, M. (2021) |
| Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyond |
| Artificial Intelligence in Medicine, 116, pp. 102073 |
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| Schindele, D., Meyer, A., Von Reibnitz, D. F., Kiesswetter, V., Schostak, M., Rak, M., Hansen, C. (2020) |
| High Resolution Prostate Segmentations for the ProstateX-Challenge [Data set] |
| The Cancer Imaging Archive, |
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| Meyer, A., Mehrtash, A., Rak, M., Schindele, S., Schostak, M., Tempany, C., Kapur, T., Abolmaesumi, P., Fedorov, A., Hansen, C. (2018) |
| Automatic High Resolution Segmentation of the Prostate from Multi-Planar MRI |
| Proceedings of IEEE International Symposium on Biomedical Imaging, Washington, D.C., USA, pp. 177-181 |
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| Meyer, A., Schindele, D., von Reibnitz, D., Rak, M., Schostak, M., Hansen, C. (2020) |
| PROSTATEx Zone Segmentations [Data set] |
| The Cancer Imaging Archive, |
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| Meyer, A., Chlebus, G., Rak, M., Schindele, D., Schostak, M., van Ginneken, B., Schenk, A., Meine, H., Hahn, H.K., Schreiber, A., Hansen, C. (2020) |
| Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI |
| Computer Methods and Programs in Biomedicine, accepted |
| Download test data set |