  | Mirizzi, G.* , Jelke, F.* , Pilot, M., Klein, K., Klamminger, G. G., GERARDY, J.-J., Theodoropoulou, M., MOMBAERTS, L., HUSCH, A., MITTELBRONN, M., HERTEL, F., & Kleine Borgmann, F. B. (06 March 2024). Impact of Formalin- and Cryofixation on Raman Spectra of Human Tissues and Strategies for Tumor Bank Inclusion. Molecules, 29 (5), 1167. doi:10.3390/molecules29051167  Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
 | Klein, K.* , Klamminger, G. G.* , MOMBAERTS, L., Jelke, F., Arroteia, I. F., Slimani, R., Mirizzi, G., HUSCH, A., FRAUENKNECHT, K., MITTELBRONN, M., HERTEL, F., & Kleine Borgmann, F. B. (23 February 2024). Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms. Molecules, 29 (5), 979. doi:10.3390/molecules29050979 Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
MASER, R., ABBAD ANDALOUSSI, M., LAMOLINE, F.* , & HUSCH, A. (2024). Unified Retrieval for Streamlining Biomedical Image Dataset Aggregation and Standardization. In Bildverarbeitung für die Medizin 2024. Springer Fachmedien Wiesbaden. doi:10.1007/978-3-658-44037-4_83 Peer reviewed* These authors have contributed equally to this work. |
 | Garcia Santa Cruz, B., Husch, A., & Hertel, F. (2023). Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions. Frontiers in Aging Neuroscience, 15. doi:10.3389/fnagi.2023.1216163 Peer reviewed |
 | Baniasadi, M., Petersen, M. V., Goncalves, J., Horn, A., Vlasov, V., Hertel, F., & Husch, A. (2022). DBSegment: Fast and robust segmentation of deep brain structures considering domain generalisation. Human Brain Mapping. doi:10.1002/hbm.26097 Peer Reviewed verified by ORBi |
Magni, S., Bremm, R. P., Lecossois, S., He, X., Garía Santa Cruz, B., Mombaerts, L., Husch, A., Goncalves, J., & Hertel, F. (05 September 2022). Analysis and comparison of gait impairments in patients with Parkinson’s disease and normal pressure hydrocephalus using wearable sensors and machine learning algorithms [Paper presentation]. 19th Biennial Meeting of the World Society for Stereotactic & Functional Neurosurgery (WSSFN 2022), Incheon, South Korea. |
 | Garcia Santa Cruz, B., Sölter, J., Gomez Giro, G., Saraiva, C., Sabaté Soler, S., Modamio Chamarro, J., Barmpa, K., Schwamborn, J. C., Hertel, F., Jarazo, J., & Husch, A. (2022). Generalising from conventional pipelines using deep learning in high‑throughput screening workfows. Scientific Reports. doi:10.1038/s41598-022-15623-7 Peer Reviewed verified by ORBi |
Abbad Andaloussi, M., Husch, A., Urcun, S., & Bordas, S. (06 June 2022). Imaging-informed BIOmechanical brain tumor forecast MOdelling [Paper presentation]. European Congres on COmputational Methods in Applied Sciences and engineering (ECCOMAS), Oslo, Norway. |
 | Garcia Santa Cruz, B., Husch, A., & Hertel, F. (2022). The effect of dataset confounding on predictions of deep neural networks for medical imaging. In Vol. 3 (2022): Proceedings of the Northern Lights Deep Learning Workshop 2022 (pp. 8). doi:10.7557/18.6302 Peer reviewed |
 | Garcia Santa Cruz, B., Bossa, M. N., Soelter, J., Hertel, F., & Husch, A. (2022). Abstract: The Importance of Dataset Choice Lessons Learned from COVID-19 X-ray Imaging Models. In Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden (pp. 114). doi:10.1007/978-3-658-36932-3_24 Peer reviewed |
Baniasadi, M., Husch, A., Proverbio, D., fernandes arroteia, I., Hertel, F., & Goncalves, J. (2022). Initialisation of Deep Brain Stimulation Parameters with Multi-objective Optimisation Using Imaging Data. In Bildverarbeitung für die Medizin 2022. Springer. doi:10.1007/978-3-658-36932-3_62 Peer reviewed |
  | Roth, H. R., Xu, Z., Diez, C. T., Jacob, R. S., Zember, J., Molto, J., Li, W., Xu, S., Turkbey, B., Turkbey, E., Yang, D., Harouni, A., Rieke, N., Hu, S., Isensee, F., Tang, C., Yu, Q., Sölter, J., Zheng, T., ... Linguraru, M. G. (2022). Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge. Medical Image Analysis, 102605. doi:10.1016/j.media.2022.102605 Peer reviewed |
 | Garcia Santa Cruz, B., Bossa, M. N., Sölter, J., & Husch, A. (December 2021). Public Covid-19 X-ray datasets and their impact on model bias - a systematic review of a significant problem. Medical Image Analysis, 74. doi:10.1016/j.media.2021.102225 Peer Reviewed verified by ORBi |
Garcia Santa Cruz, B., Bossa, M. N., Sölter, J., Husch, A., & Hertel, F. (August 2021). Model bias and its impact on computer-aided diagnosis: A data-centric approach [Poster presentation]. 2021 MLSS. doi:10.5281/zenodo.5205671 |
 | Kemp, F., Proverbio, D., Aalto, A., Mombaerts, L., Fouquier d'herouël, A., Husch, A., Ley, C., Goncalves, J., Skupin, A., & Magni, S. (2021). Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden. Journal of Theoretical Biology. doi:10.1016/J.JTBI.2021.110874 Peer reviewed |
 | Sölter, J., Proverbio, D., Baniasadi, M., Bossa, M. N., Vlasov, V., Garcia Santa Cruz, B., & Husch, A. (2021). Leveraging state-of-the-art architectures by enriching training information - a case study [Paper presentation]. COVID 19-20 Lung CT Lesion Segmentation Grand Challenge Mini-symposium, United States. |
 | Proverbio, D., Kemp, F., Magni, S., Husch, A., Aalto, A., Mombaerts, L., Skupin, A., Goncalves, J., Ameijeiras-Alonso, J., & Ley, C. (2021). Dynamical SPQEIR model assesses the effectiveness of non-pharmaceutical interventions against COVID-19 epidemic outbreaks. PLoS ONE, 16 (5), 0252019. doi:10.1371/journal.pone.0252019 Peer Reviewed verified by ORBi |
 | Klamminger, G. G., Gerardy, J.-J., Jelke, F., Mirizzi, G., Slimani, R., Klein, K., Husch, A., Hertel, F., Mittelbronn, M., & Kleine-Borgmann, F. B. (2021). Application of Raman Spectroscopy for Detection of Histologically Distinct Areas in Formalin-fixed Paraffin-embedded (FFPE) Glioblastoma. Neuro-Oncology Advances. doi:10.1093/noajnl/vdab077 Peer Reviewed verified by ORBi |
 | Roth, H., Xu, Z., Diez, C. T., Jacob, R. S., Zember, J., Molto, J., Li, W., Xu, S., Turkbey, B., Turkbey, E., Yang, D., Harouni, A., Rieke, N., Hu, S., Isensee, F., Tang, C., Yu, Q., Sölter, J., Zheng, T., ... Linguraru, M. (2021). Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/47586. |
 | Vlasov, V., Bofferding, M., Marx, L. M., Zhang, C., Goncalves, J., Husch, A., & Hertel, F. (2021). Automated Deep Learning-based Segmentation of Brain, SEEG and DBS Electrodes on CT Images. In Bildverarbeitung für die Medizin 2021 (pp. 92-97). doi:10.1007/978-3-658-33198-6_22 Peer reviewed |
Husch, A., & Hertel, F. (2021). DBS Imaging Methods II: Electrode Localization. In A. Horn (Ed.), Connectomic Deep Brain Stimulation (1st ed). Elsevier. doi:10.1016/B978-0-12-821861-7.00004-X |
 | Jelke, F., Mirizzi, G., Borgmann, F. K., Husch, A., Slimani, R., Klamminger, G. G., Klein, K., Mombaerts, L., Gerardy, J.-J., Mittelbronn, M., & Hertel, F. (2021). Intraoperative discrimination of native meningioma and dura mater by Raman spectroscopy. Scientific Reports, 1--10. doi:10.1038/s41598-021-02977-7 Peer Reviewed verified by ORBi |
 | Klamminger, G. G., Klein, K., Mombaerts, L., Jelke, F., Mirizzi, G., Slimani, R., Husch, A., Mittelbronn, M., Hertel, F., & Borgmann, F. B. K. (2021). Differentiation of primary CNS lymphoma and glioblastoma using Raman spectroscopy and machine learning algorithms. Free Neuropathology, 2, 26-26. doi:10.17879/freeneuropathology-2021-3458 Peer Reviewed verified by ORBi |
 | Garcia Santa Cruz, B., Husch, A., & Hertel, F. (2020). Automatic Detection of Nigrosome Degeneration in Susceptibility-Weighted MRI for Computer-Aided Diagnosis of Parkinson’s Disease Using Machine Learning. Movement Disorders. doi:10.1002/mds.28267 Peer reviewed |
 | Baniasadi, M., Proverbio, D., Goncalves, J., Hertel, F., & Husch, A. (2020). FastField: An Open-Source Toolbox for Efficient Approximation of Deep Brain Stimulation Electric Fields. NeuroImage. doi:10.1016/j.neuroimage.2020.117330 Peer Reviewed verified by ORBi |
 | Garcia Santa Cruz, B., Sölter, J., Bossa, M. N., & Husch, A. (2020). On the Composition and Limitations of Publicly Available COVID-19 X-Ray Imaging Datasets. (1). ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/44138. |
 | Proverbio, D., Kemp, F., Magni, S., Husch, A., Aalto, A., Mombaerts, L., Goncalves, J., Skupin, A., Ameijeiras-Alonso, J., & Ley, C. (2020). Assessing suppression strategies against epidemicoutbreaks like COVID-19: the SPQEIR model. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/44206. doi:10.1101/2020.04.22.20075804doi: |
 | Arroteia, I. F., Husch, A., Baniasadi, M., & Hertel, F. (2020). Impressive weight gain after deep brain stimulation of nucleus accumbens in treatment- resistant bulimic anorexia nervosa. BMJ Case Reports, 1--4. doi:10.1136/bcr-2020-239316 Peer Reviewed verified by ORBi |
 | Garcia Santa Cruz, B., Jarazo, J., Saraiva, C., Gomez Giro, G., Modamio Chamarro, J., Sabaté Soler, S., Kyriaki, B., Antony, P., Schwamborn, J. C., Hertel, F., & Husch, A. (29 November 2019). From tech to bench: Deep Learning pipeline for image segmentation of high-throughput high-content microscopy data [Poster presentation]. Advances in Computational Biology, Barcelona, Spain. |
 | Garcia Santa Cruz, B., Jarazo, J., Schwamborn, J. C., Hertel, F., & Husch, A. (10 October 2019). Deep Learning Quality Control for High-Throughput High-Content Screening Microscopy Images [Poster presentation]. EMBO|EMBL Symposia - Seeing is Believing - Imaging the Molecular Processes of Life, Heidelberg, Germany. |
 | Zhang, C., Kim, S.-G., Li, D., Zhang, Y., Li, Y., Husch, A., Hertel, F., Yan, F., Voon, V., & Sun, B. (2019). Habenula deep brain stimulation for refractory bipolar disorder. Brain Stimulation. doi:10.1016/j.brs.2019.05.010 Peer Reviewed verified by ORBi |
Kleine Borgmann, F., Husch, A., Slimani, R., Jelke, F., Mirizzi, G., Klein, K., Mittelbronn, M., & Hertel, F. (2019). PATH-29. POTENTIAL OF RAMAN SPECTROSCOPY IN ONCOLOGICAL NEUROSURGERY [Poster presentation]. 24th Annual Scientific Meeting and Education Day of the Society for Neuro-Oncology, Phoenix, Arizona, United States. doi:10.1093/neuonc/noz175.625 |
 | Proverbio, D., & Husch, A. (2019). ApproXON: Heuristic Approximation to the E-Field-Threshold for Deep Brain Stimulation Volume-of-Tissue-Activated Estimation. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/41183. doi:10.1101/863613 |
 | Husch, A., Petersen, M. V., Gemmar, P., Goncalves, J., & Hertel, F. (2018). PaCER - A fully automated method for electrode trajectory and contact reconstruction in deep brain stimulation. NeuroImage: Clinical, 17, 80 - 89. doi:10.1016/j.nicl.2017.10.004 Peer Reviewed verified by ORBi |
 | Husch, A., Petersen, M. V., Gemmar, P., Goncalves, J., Sunde, N., & Hertel, F. (2018). Post-operative deep brain stimulation assessment: Automatic data integration and report generation. Brain Stimulation. doi:10.1016/j.brs.2018.01.031 Peer reviewed |
 | Petersen, M. V., Husch, A., Parsons, C. E., Lund, T. E., Sunde, N., & Østergaard, K. (2018). Using automated electrode localization to guide stimulation management in DBS. Annals of Clinical and Translational Neurology, 0 (0). doi:10.1002/acn3.589 Peer reviewed |
 | Horn, A., Li, N., Dembek, T. A., Kappel, A., Boulay, C., Ewert, S., Tietze, A., Husch, A., Perera, T., Neumann, W.-J., Reisert, M., Si, H., Oostenveld, R., Rorden, C., Yeh, F.-C., Fang, Q., Herrington, T. M., Vorwerk, J., & Kuhn, A. A. (2018). Lead-DBS v2: Towards a comprehensive pipeline for deep brain stimulation imaging. NeuroImage. doi:10.1016/j.neuroimage.2018.08.068 Peer Reviewed verified by ORBi |
 | Husch, A., Gemmar, P., Thunberg, J., & Hertel, F. (2017). Integration of sparse electrophysiological measurements with preoperative MRI using 3D surface estimation in deep brain stimulation surgery. In R. Webster & B. Fei (Eds.), Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling (pp. 10135-16). SPIE. doi:10.1117/12.2255894 Peer reviewed |
 | Bernard, F., Vlassis, N., Gemmar, P., Husch, A., Thunberg, J., Goncalves, J., & Hertel, F. (2016). Fast Correspondences for Statistical Shape Models of Brain Structures. In SPIE Medical Imaging. doi:10.1117/12.2206024 Peer reviewed |
 | Bernard, F., Thunberg, J., Gemmar, P., Hertel, F., Husch, A., & Goncalves, J. (2015). A solution for Multi-Alignment by Transformation Synchronisation. In The proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. Peer reviewed |
 | Bernard, F., Thunberg, J., Salamanca Mino, L., Gemmar, P., Hertel, F., Goncalves, J., & Husch, A. (2015). Transitively Consistent and Unbiased Multi-Image Registration Using Numerically Stable Transformation Synchronisation. MIDAS Journal. Peer reviewed |
 | Husch, A., Gemmar, P., Lohscheller, J., Bernard, F., & Hertel, F. (2015). Assessment of Electrode Displacement and Deformation with Respect to Pre-Operative Planning in Deep Brain Stimulation. In H. Handels, T. M. Deserno, H.-P. Meinzer, ... T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2015 (pp. 77-82). Springer Berlin Heidelberg. doi:10.1007/978-3-662-46224-9_15 Peer reviewed |
 | Hertel, F., Husch, A., Dooms, G., Bernard, F., & Gemmar, P. (2015). Susceptibility-Weighted MRI for Deep Brain Stimulation: Potentials in Trajectory Planning. Stereotactic and Functional Neurosurgery, 93 (5), 303-308. doi:10.1159/000433445 Peer Reviewed verified by ORBi |
 | Bernard, F., Gemmar, P., Husch, A., Saleh, C., Neb, H., Dooms, G., & Hertel, F. (2014). Improving the Consistency of Manual Deep Brain Structure Segmentations by Combining Variational Interpolation, Simultaneous Multi-Modality Visualisation and Histogram Equilisation. Biomedizinische Technik. Biomedical Engineering, 59 (1), 131-134. doi:10.1515/bmt-2014-5008 Peer reviewed |
 | Bernard, F., Gemmar, P., Husch, A., & Hertel, F. (2014). An Extensible Development Environment for 3D Segmentations based on Active Shape Models. In Shape Symposium (pp. 39). Peer reviewed |
 | Hana, A., Husch, A., Gunness, V. R. N., Berthold, C., Hana, A., Dooms, G., Boecher Schwarz, H., & Hertel, F. (2014). DTI of the visual pathway - white matter tracts and cerebral lesions. Journal of Visualized Experiments, (90). doi:10.3791/51946 Peer Reviewed verified by ORBi |
Husch, A. (n.d.). Data Integration for Image Guided Deep Brain Stimulation [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/34514 |
