Prof. Dr. Lena Maier-Hein

DKFZ

Lena Maier-Hein is one of Helmholtz Imaging’s center coordinators at the German Cancer Research Center (DKFZ) as well as head of the division Intelligent Medical Systems (IMSY) and managing director of the “Data Science and Digital Oncology” cross-topic program. She is a full professor at Heidelberg University (Germany) and managing director of the National Center for Tumor Diseases (NCT) Heidelberg.

Her research concentrates on machine learning-based biomedical image analysis with a specific focus on surgical data science, computational biophotonics and validation of machine learning algorithms. She is a fellow of the Medical Image Computing and Computer Assisted Intervention (MICCAI) society and of the European Laboratory for Learning and Intelligent Systems (ELLIS), president of the MICCAI special interest group on challenges and chair of the international surgical data science initiative.

Lena Maier-Hein serves on the editorial board of the journals Nature Scientific Data, IEEE Transactions on Pattern Analysis and Machine Intelligence and Medical Image Analysis. During her academic career, she has been distinguished with several science awards including the 2013 Heinz Maier Leibnitz Award of the German Research Foundation (DFG) and the 2017/18 Berlin-Brandenburg Academy Prize. She has received a European Research Council (ERC) starting grant (2015-2020) and consolidator grant (2021-2026).

Publications

Mais, L., Hirsch, P., Managan, C., Kandarpa, R., Rumberger, J. L., Reinke, A., Maier-Hein, L., Ihrke, G., & Kainmueller, D. (2024). FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures (arXiv:2404.00130). arXiv. https://doi.org/10.48550/arXiv.2404.00130
Reinke, A., Tizabi, M. D., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Sudre, C. H., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Benis, A., Buettner, F., Cardoso, M. J., Cheplygina, V., Chen, J., Christodoulou, E., Cimini, B. A., … Maier-Hein, L. (2024). Understanding metric-related pitfalls in image analysis validation. Nature Methods, 1–13. https://doi.org/10.1038/s41592-023-02150-0
Yang, K., Musio, F., Ma, Y., Juchler, N., Paetzold, J. C., Al-Maskari, R., Höher, L., Li, H. B., Hamamci, I. E., Sekuboyina, A., Shit, S., Huang, H., Waldmannstetter, D., Kofler, F., Navarro, F., Menten, M., Ezhov, I., Rueckert, D., Vos, I., … Menze, B. (2024). TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA (arXiv:2312.17670). arXiv. https://doi.org/10.48550/arXiv.2312.17670
Gotkowski, K., Gupta, S., Godinho, J. R. A., Tochtrop, C. G. S., Maier-Hein, K. H., & Isensee, F. (2024). ParticleSeg3D: A scalable out-of-the-box deep learning segmentation solution for individual particle characterization from micro CT images in mineral processing and recycling. Powder Technology, 434, 119286. https://doi.org/10.1016/j.powtec.2023.119286
Koehler, G., Wald, T., Ulrich, C., Zimmerer, D., Jaeger, P. F., Franke, J. K., Kohl, S., Isensee, F., & Maier-Hein, K. H. (2024). RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 810–818. https://openaccess.thecvf.com/content/WACV2024/html/Kohler_RecycleNet_Latent_Feature_Recycling_Leads_to_Iterative_Decision_Refinement_WACV_2024_paper.html
Kahl, K.-C., Lüth, C. T., Zenk, M., Maier-Hein, K., & Jaeger, P. F. (2024). ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation. https://doi.org/10.48550/ARXIV.2401.08501
Almeida, S. D., Norajitra, T., Lüth, C. T., Wald, T., Weru, V., Nolden, M., Jäger, P. F., von Stackelberg, O., Heußel, C. P., Weinheimer, O., Biederer, J., Kauczor, H.-U., & Maier-Hein, K. (2023). Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT. European Radiology. https://doi.org/10.1007/s00330-023-10540-3
Isensee, F., & Maier-Hein, K. H. (2023). Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans (arXiv:2309.13747). arXiv. https://doi.org/10.48550/arXiv.2309.13747
Kovacs, B., Netzer, N., Baumgartner, M., Schrader, A., Isensee, F., Weißer, C., Wolf, I., Görtz, M., Jaeger, P. F., Schütz, V., Floca, R., Gnirs, R., Stenzinger, A., Hohenfellner, M., Schlemmer, H.-P., Bonekamp, D., & Maier-Hein, K. H. (2023). Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer. Scientific Reports, 13(1), 19805. https://doi.org/10.1038/s41598-023-46747-z
Brandenburg, J. M., Jenke, A. C., Stern, A., Daum, M. T. J., Schulze, A., Younis, R., Petrynowski, P., Davitashvili, T., Vanat, V., Bhasker, N., Schneider, S., Mündermann, L., Reinke, A., Kolbinger, F. R., Jörns, V., Fritz-Kebede, F., Dugas, M., Maier-Hein, L., Klotz, R., … Wagner, M. (2023). Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study. Surgical Endoscopy, 37(11), 8577–8593. https://doi.org/10.1007/s00464-023-10447-6
Adler, T. J., Nölke, J.-H., Reinke, A., Tizabi, M. D., Gruber, S., Trofimova, D., Ardizzone, L., Jaeger, P. F., Buettner, F., Köthe, U., & Maier-Hein, L. (2023). Application-driven Validation of Posteriors in Inverse Problems (arXiv:2309.09764). arXiv. https://doi.org/10.48550/arXiv.2309.09764
Kovacs, B., Netzer, N., Baumgartner, M., Eith, C., Bounias, D., Meinzer, C., Jaeger, P. F., Zhang, K. S., Floca, R., Schrader, A., Isensee, F., Gnirs, R., Goertz, M., Schuetz, V., Stenzinger, A., Hohenfellner, M., Schlemmer, H.-P., Wolf, I., Bonekamp, D., & Maier-Hein, K. H. (2023). Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection (arXiv:2309.03652). arXiv. https://doi.org/10.48550/arXiv.2309.03652
Brugnara, G., Baumgartner, M., Scholze, E. D., Deike-Hofmann, K., Kades, K., Scherer, J., Denner, S., Meredig, H., Rastogi, A., Mahmutoglu, M. A., Ulfert, C., Neuberger, U., Schönenberger, S., Schlamp, K., Bendella, Z., Pinetz, T., Schmeel, C., Wick, W., Ringleb, P. A., … Vollmuth, P. (2023). Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke. Nature Communications, 14(1), 4938. https://doi.org/10.1038/s41467-023-40564-8
Bounias, D., Baumgartner, M., Neher, P., Kovacs, B., Floca, R., Jaeger, P. F., Kapsner, L., Eberle, J., Hadler, D., Laun, F., Ohlmeyer, S., Maier-Hein, K., & Bickelhaupt, S. (2023, July 19). Risk-adjusted Training and Evaluation for Medical Object Detection in Breast Cancer MRI. ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH). https://openreview.net/forum?id=WwceaG9wOU#all
Almeida, S. D., Lüth, C. T., Norajitra, T., Wald, T., Nolden, M., Jaeger, P. F., Heussel, C. P., Biederer, J., Weinheimer, O., & Maier-Hein, K. (2023). cOOpD: Reformulating COPD classification on chest CT scans as anomaly detection using contrastive representations (arXiv:2307.07254). arXiv. https://doi.org/10.48550/arXiv.2307.07254
Wald, T., Ulrich, C., Isensee, F., Zimmerer, D., Koehler, G., Baumgartner, M., & Maier-Hein, K. H. (2023). Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency (arXiv:2307.02516). arXiv. https://doi.org/10.48550/arXiv.2307.02516
Tran, T. N., Adler, T. J., Yamlahi, A., Christodoulou, E., Godau, P., Reinke, A., Tizabi, M. D., Sauer, P., Persicke, T., Albert, J. G., & Maier-Hein, L. (2023). Sources of performance variability in deep learning-based polyp detection. International Journal of Computer Assisted Radiology and Surgery, 18(7), 1311–1322. https://doi.org/10.1007/s11548-023-02936-9
Weikert, T., Jaeger, P. F., Yang, S., Baumgartner, M., Breit, H. C., Winkel, D. J., Sommer, G., Stieltjes, B., Thaiss, W., Bremerich, J., Maier-Hein, K. H., & Sauter, A. W. (2023). Automated lung cancer assessment on 18F-PET/CT using Retina U-Net and anatomical region segmentation. European Radiology, 33(6), 4270–4279. https://doi.org/10.1007/s00330-022-09332-y
Wagner, M., Müller-Stich, B.-P., Kisilenko, A., Tran, D., Heger, P., Mündermann, L., Lubotsky, D. M., Müller, B., Davitashvili, T., Capek, M., Reinke, A., Reid, C., Yu, T., Vardazaryan, A., Nwoye, C. I., Padoy, N., Liu, X., Lee, E.-J., Disch, C., … Bodenstedt, S. (2023). Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark. Medical Image Analysis, 86, 102770. https://doi.org/10.1016/j.media.2023.102770
Holzschuh, J., Zimmerer, D., Ulrich, C., Baumgartner, M., Koehler, G., Stiefelhagen, R., & Maier-Hein, K. (2023, April 28). Combining Anomaly Detection and Supervised Learning for Medical Image Segmentation. Medical Imaging with Deep Learning, short paper track. https://openreview.net/forum?id=OytzS_LCWvw
Roy, S., Koehler, G., Baumgartner, M., Ulrich, C., Petersen, J., Isensee, F., & Maier-Hein, K. (2023). Transformer Utilization in Medical Image Segmentation Networks (arXiv:2304.04225). arXiv. https://doi.org/10.48550/arXiv.2304.04225
Eisenmann, M., Reinke, A., Weru, V., Tizabi, M. D., Isensee, F., Adler, T. J., Ali, S., Andrearczyk, V., Aubreville, M., Baid, U., Bakas, S., Balu, N., Bano, S., Bernal, J., Bodenstedt, S., Casella, A., Cheplygina, V., Daum, M., de Bruijne, M., … Maier-Hein, L. (2023). Why is the winner the best? (arXiv:2303.17719). arXiv. https://doi.org/10.48550/arXiv.2303.17719
Baumgartner, M., Full, P., & Maier-Hein, K. (2023). Accurate Detection of Mediastinal Lesions with nnDetection.
Vollmuth, P., Foltyn, M., Huang, R. Y., Galldiks, N., Petersen, J., Isensee, F., van den Bent, M. J., Barkhof, F., Park, J. E., Park, Y. W., Ahn, S. S., Brugnara, G., Meredig, H., Jain, R., Smits, M., Pope, W. B., Maier-Hein, K., Weller, M., Wen, P. Y., … Bendszus, M. (2023). Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study. Neuro-Oncology, 25(3), 533–543. https://doi.org/10.1093/neuonc/noac189
Ayala, L., Adler, T. J., Seidlitz, S., Wirkert, S., Engels, C., Seitel, A., Sellner, J., Aksenov, A., Bodenbach, M., Bader, P., Baron, S., Vemuri, A., Wiesenfarth, M., Schreck, N., Mindroc, D., Tizabi, M., Pirmann, S., Everitt, B., Kopp-Schneider, A., … Maier-Hein, L. (2023). Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery. Science Advances, 9(10), eadd6778. https://doi.org/10.1126/sciadv.add6778
Rädsch, T., Reinke, A., Weru, V., Tizabi, M. D., Schreck, N., Kavur, A. E., Pekdemir, B., Roß, T., Kopp-Schneider, A., & Maier-Hein, L. (2023). Labelling instructions matter in biomedical image analysis. Nature Machine Intelligence, 1–11. https://doi.org/10.1038/s42256-023-00625-5
Roß, T., Bruno, P., Reinke, A., Wiesenfarth, M., Koeppel, L., Full, P. M., Pekdemir, B., Godau, P., Trofimova, D., Isensee, F., Adler, T. J., Tran, T. N., Moccia, S., Calimeri, F., Müller-Stich, B. P., Kopp-Schneider, A., & Maier-Hein, L. (2023). Beyond rankings: Learning (more) from algorithm validation. Medical Image Analysis, 102765. https://doi.org/10.1016/j.media.2023.102765
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., Lohöfer, F., Holch, J. W., Sommer, W., Hofmann, F., Hostettler, A., Lev-Cohain, N., Drozdzal, M., Amitai, M. M., Vivanti, R., … Menze, B. (2023). The Liver Tumor Segmentation Benchmark (LiTS). Medical Image Analysis, 84, 102680. https://doi.org/10.1016/j.media.2022.102680
Lüth, C. T., Zimmerer, D., Koehler, G., Jaeger, P. F., Isensee, F., Petersen, J., & Maier-Hein, K. H. (2023). CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization (arXiv:2301.02126). arXiv. https://doi.org/10.48550/arXiv.2301.02126
Nwoye, C. I., Yu, T., Sharma, S., Murali, A., Alapatt, D., Vardazaryan, A., Yuan, K., Hajek, J., Reiter, W., Yamlahi, A., Smidt, F.-H., Zou, X., Zheng, G., Oliveira, B., Torres, H. R., Kondo, S., Kasai, S., Holm, F., Özsoy, E., … Padoy, N. (2023). CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection. Medical Image Analysis, 89, 102888. https://doi.org/10.1016/j.media.2023.102888
Maška, M., Ulman, V., Delgado-Rodriguez, P., Gómez-de-Mariscal, E., Nečasová, T., Guerrero Peña, F. A., Ren, T. I., Meyerowitz, E. M., Scherr, T., Löffler, K., Mikut, R., Guo, T., Wang, Y., Allebach, J. P., Bao, R., Al-Shakarji, N. M., Rahmon, G., Toubal, I. E., Palaniappan, K., … Ortiz-de-Solórzano, C. (2023). The Cell Tracking Challenge: 10 years of objective benchmarking. Nature Methods, 20(7), 1010–1020. https://doi.org/10.1038/s41592-023-01879-y
Lüth, C. T., Zimmerer, D., Koehler, G., Jaeger, P. F., Isenensee, F., & Maier-Hein, K. H. (2023). Contrastive Representations for Unsupervised Anomaly Detection and Localization. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 246–252). Springer Fachmedien. https://doi.org/10.1007/978-3-658-41657-7_54
Lüth, C. T., Zimmerer, D., Koehler, G., Jaeger, P. F., Isenensee, F., & Maier-Hein, K. H. (2023). Contrastive Representations for Unsupervised Anomaly Detection and Localization. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 246–252). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_54
Isensee, F., Ulrich, C., Wald, T., & Maier-Hein, K. H. (2023). Extending nnU-Net Is All You Need. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 12–17). Springer Fachmedien. https://doi.org/10.1007/978-3-658-41657-7_7
Godau, P., Kalinowski, P., Christodoulou, E., Reinke, A., Tizabi, M., Ferrer, L., Jäger, P., & Maier-Hein, L. (2023). Deployment of Image Analysis Algorithms under Prevalence Shifts. https://doi.org/10.48550/ARXIV.2303.12540
Klein, L., Ziegler, S., Laufer, F., Debus, C., Götz, M., Maier-Hein, K., Paetzold, U., Isensee, F., & Jaeger, P. (2023). Understanding Scalable Perovskite Solar Cell Manufacturing with Explainable AI. https://publikationen.bibliothek.kit.edu/1000167169
Ulrich, C., Isensee, F., Wald, T., Zenk, M., Baumgartner, M., & Maier-Hein, K. H. (2023). MultiTalent: A Multi-dataset Approach to Medical Image Segmentation. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (pp. 648–658). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43898-1_62
Ickler, M. K., Baumgartner, M., Roy, S., Wald, T., & Maier-Hein, K. H. (2023). Taming Detection Transformers for Medical Object Detection. In T. M. Deserno, H. Handels, A. Maier, K. Maier-Hein, C. Palm, & T. Tolxdorff (Eds.), Bildverarbeitung für die Medizin 2023 (pp. 183–188). Springer Fachmedien. https://doi.org/10.1007/978-3-658-41657-7_39
Roy, S., Koehler, G., Ulrich, C., Baumgartner, M., Petersen, J., Isensee, F., Jaeger, P. F., & Maier-Hein, K. (2023). MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation. https://doi.org/10.48550/ARXIV.2303.09975
Eisenmann, M., Reinke, A., Weru, V., Tizabi, M. D., Isensee, F., Adler, T. J., Godau, P., Cheplygina, V., Kozubek, M., Ali, S., Gupta, A., Kybic, J., Noble, A., de Solórzano, C. O., Pachade, S., Petitjean, C., Sage, D., Wei, D., Wilden, E., … Maier-Hein, L. (2022). Biomedical image analysis competitions: The state of current participation practice (arXiv:2212.08568). arXiv. https://doi.org/10.48550/arXiv.2212.08568
Tran, T. N., Adler, T., Yamlahi, A., Christodoulou, E., Godau, P., Reinke, A., Tizabi, M. D., Sauer, P., Persicke, T., Albert, J. G., & Maier-Hein, L. (2022). Sources of performance variability in deep learning-based polyp detection (arXiv:2211.09708). arXiv. https://doi.org/10.48550/arXiv.2211.09708
Roth, H. R., Xu, Z., Tor-Díez, C., Sanchez Jacob, R., 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, 82, 102605. https://doi.org/10.1016/j.media.2022.102605
Zimmerer, D., Full, P. M., Isensee, F., Jäger, P., Adler, T., Petersen, J., Köhler, G., Ross, T., Reinke, A., Kascenas, A., Jensen, B. S., O’Neil, A. Q., Tan, J., Hou, B., Batten, J., Qiu, H., Kainz, B., Shvetsova, N., Fedulova, I., … Maier-Hein, K. (2022). MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images. IEEE Transactions on Medical Imaging, 41(10), 2728–2738. https://doi.org/10.1109/TMI.2022.3170077
Maier-Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., Reyes, M., Riegler, M. A., Wiesenfarth, M., Baumgartner, M., Eisenmann, M., Heckmann-Nötzel, D., Kavur, A. E., Rädsch, T., Acion, L., Antonelli, M., … Jäger, P. F. (2022). Metrics reloaded: Pitfalls and recommendations for image analysis validation (arXiv:2206.01653). arXiv. https://doi.org/10.48550/arXiv.2206.01653
Koehler, G., Isensee, F., & Maier-Hein, K. (2022). A Noisy nnU-Net Student for Semi-supervised Abdominal Organ Segmentation. https://openreview.net/forum?id=-XzpY3MyKPU
Isensee, F., Ulrich, C., Wald, T., & Maier-Hein, K. H. (2022). Extending nnU-Net is all you need (arXiv:2208.10791). arXiv. https://doi.org/10.48550/arXiv.2208.10791
Vollmuth, P., Foltyn, M., Huang, R. Y., Galldiks, N., Petersen, J., Isensee, F., van den Bent, M. J., Barkhof, F., Park, J. E., Park, Y. W., Ahn, S. S., Brugnara, G., Meredig, H., Jain, R., Smits, M., Pope, W. B., Maier-Hein, K., Weller, M., Wen, P. Y., … Bendszus, M. (2022). AI-based decision support improves reproducibility of tumor response assessment in neuro-oncology: an international multi-reader study. Neuro-Oncology, noac189. https://doi.org/10.1093/neuonc/noac189
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., van Ginneken, B., Bilello, M., Bilic, P., Christ, P. F., Do, R. K. G., Gollub, M. J., Heckers, S. H., Huisman, H., Jarnagin, W. R., … Cardoso, M. J. (2022). The Medical Segmentation Decathlon. Nature Communications, 13(1), 4128. https://doi.org/10.1038/s41467-022-30695-9
Reinke, A., Tizabi, M. D., Sudre, C. H., Eisenmann, M., Rädsch, T., Baumgartner, M., Acion, L., Antonelli, M., Arbel, T., Bakas, S., Bankhead, P., Benis, A., Cardoso, M. J., Cheplygina, V., Christodoulou, E., Cimini, B., Collins, G. S., Farahani, K., van Ginneken, B., … Maier-Hein, L. (2022). Common Limitations of Image Processing Metrics: A Picture Story (arXiv:2104.05642). arXiv. https://doi.org/10.48550/arXiv.2104.05642
Schellenberg, M., Dreher, K. K., Holzwarth, N., Isensee, F., Reinke, A., Schreck, N., Seitel, A., Tizabi, M. D., Maier-Hein, L., & Gröhl, J. (2022). Semantic segmentation of multispectral photoacoustic images using deep learning. Photoacoustics, 26, 100341. https://doi.org/10.1016/j.pacs.2022.100341

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