Profil

HUSCH Andreas

University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Imaging AI

ORCID
0000-0001-9404-5127
Main Referenced Co-authors
HERTEL, Frank  (32)
GONCALVES, Jorge  (13)
GARCIA SANTA CRUZ, Beatriz  (10)
Gemmar, Peter (10)
BERNARD, Florian  (7)
Main Referenced Keywords
COVID-19 (4); deep brain stimulation (4); Deep Learning (3); Machine Learning (3); Analytical Chemistry (2);
Main Referenced Unit & Research Centers
Luxembourg Centre for Systems Biomedicine (LCSB): Systems Control (Goncalves Group) (7)
Luxembourg Centre for Systems Biomedicine (LCSB) (2)
Centre Hospitalier de Luxembourg: National Department of Neurosurgery (Hertel Group) (1)
Centre Hospitalier du Luxembourg and Luxembourg Centre for Systems Biomedicine (1)
Luxembourg Centre for Systems Biomedicine (LCSB): Interventional Neuroscience (1)
Main Referenced Disciplines
Engineering, computing & technology: Multidisciplinary, general & others (15)
Human health sciences: Multidisciplinary, general & others (10)
Computer science (9)
Life sciences: Multidisciplinary, general & others (9)
Oncology (4)

Publications (total 47)

The most downloaded
407 downloads
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 https://hdl.handle.net/10993/46439

The most cited

469 citations (Scopus®)

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 https://hdl.handle.net/10993/36554

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

KEMP, F., PROVERBIO, D., AALTO, A., MOMBAERTS, L., FOUQUIER DU0027HEROUËL, 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

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

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., {"lastName":"GERARDY", F. J.-J. A. U. O. L. U. E. L. C. F. S. B. L. T. T., 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., {"lastName":"HUSCH", F. A. A. U. O. L. U. E. L. C. F. S. B. L. U. E. I. N. D. V. N., & 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

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

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

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

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.

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

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:

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

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

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

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

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

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

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

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