Alexander Jaus

Alexander Jaus

Postdoctoral Researcher in Computer Vision

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publications

Towards unifying anatomy segmentation: automated generation of a full-body ct dataset via knowledge aggregation and anatomical guidelines

Published in arXiv, 2023

Recommended citation: Jaus, Alexander; Seibold, Constantin; Hermann, Kelsey; Walter, Alexandra; Giske, Kristina; Haubold, Johannes; Kleesiek, Jens; Stiefelhagen, Rainer; . (2023). "Towards unifying anatomy segmentation: automated generation of a full-body ct dataset via knowledge aggregation and anatomical guidelines." arXiv preprint arXiv:2307.13375..
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Anatomy-guided Pathology Segmentation

Published in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024

Recommended citation: Jaus, Alexander; Seibold, Constantin; Reiß, Simon; Heine, Lukas; Schily, Anton; Kim, Moon; Bahnsen, Fin Hendrik; Herrmann, Ken; Stiefelhagen, Rainer; Kleesiek, Jens; . (2024). "Anatomy-guided Pathology Segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention: 3-13.
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FootCapture: Towards an AR-based System for 3D Foot Object Acquisition through Photogrammetry

Published in Medical Imaging with Deep Learning, 2024

Recommended citation: Khan-Blouki, Valentin; Seiz, Franziska; Walter, Nicolas; Jaus, Alexander; Marinov, Zdravko; Luijten, Gijs; Egger, Jan; Seibold, Constantin Marc; Solte, Dirk; Kleesiek, Jens; Stiefelhagen, Rainer. (2024). "FootCapture: Towards an AR-based System for 3D Foot Object Acquisition through Photogrammetry." Medical Imaging with Deep Learning
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Muscles in time: Learning to understand human motion in-depth by simulating muscle activations

Published in Advances in Neural Information Processing Systems, 2024

Recommended citation: Schneider, David; Reiß, Simon; Kugler, Marco; Jaus, Alexander; Peng, Kunyu; Sutschet, Susanne; Sarfraz, M Saquib; Matthiesen, Sven; Stiefelhagen, Rainer; . (2024). "Muscles in time: Learning to understand human motion in-depth by simulating muscle activations." Advances in Neural Information Processing Systems. 37: 67251-67281.
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Towards Unifying Anatomy Segmentation: Automated Generation of a Full-Body CT Dataset

Published in 2024 IEEE International Conference on Image Processing (ICIP), 2024

Recommended citation: Jaus, Alexander; Seibold, Constantin; Hermann, Kelsey; Shahamiri, Negar; Walter, Alexandra; Giske, Kristina; Haubold, Johannes; Kleesiek, Jens; Stiefelhagen, Rainer; . (2024). "Towards Unifying Anatomy Segmentation: Automated Generation of a Full-Body CT Dataset." 2024 IEEE International Conference on Image Processing (ICIP): 41-47.
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Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks

Published in Proceedings of the AAAI Conference on Artificial Intelligence 2025, 2025

Recommended citation: Jaus, Alexander; Seibold, Constantin; Reiß, Simon; Marinov, Zdravko; Li, Keyi; Ye, Zeling; Krieg, Stefan; Kleesiek, Jens; Stiefelhagen, Rainer; . (2024). "Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks." Proceedings of the AAAI Conference on Artificial Intelligence 2025.
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LIMIS: Towards Language-based Interactive Medical Image Segmentation (Oral)

Published in International Symposium on Biomedical Imaging (ISBI), 2025

Recommended citation: Heinemann, L., Jaus, A., Marinov, Z., Kim, M., Spadea, M. F., Kleesiek, J., & Stiefelhagen, R. (2025, April). LIMIS: Towards Language-Based Interactive Medical Image Segmentation. In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE.
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Medshapenet–a large-scale dataset of 3d medical shapes for computer vision

Published in Biomedical Engineering/Biomedizinische Technik, 2025

Recommended citation: Li, Jianning; Zhou, Zongwei; Yang, Jiancheng; Pepe, Antonio; Gsaxner, Christina; Luijten, Gijs; Qu, Chongyu; Zhang, Tiezheng; Chen, Xiaoxi; Li, Wenxuan; . (2025). "Medshapenet–a large-scale dataset of 3d medical shapes for computer vision." Biomedical Engineering/Biomedizinische Technik. 70(1): 71-90.
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Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement

Published in Statistical Atlases and Computational Models of the Heart (STACOM), held with MICCAI 2025, 2025

When preoperative planning for surgeries is conducted on the basis of medical images, artificial intelligence methods can support medical doctors during assessment. In this work, we consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks that may be supported via semantic segmentation models by making relevant anatomical structures measurable in computed tomography scans.

Recommended citation: Zöllner, C., Reiß, S., Jaus, A., Sholi, A., Sodian, R., & Stiefelhagen, R. (2025). Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement. Statistical Atlases and Computational Models of the Heart (STACOM), held with MICCAI 2025.
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GRASPing Anatomy to Improve Pathology Segmentation

Published in Machine Learning in Medical Imaging (MLMI), held with MICCAI 2025, 2025

Radiologists rely on anatomical understanding to accurately delineate pathologies, yet most current deep learning approaches use pure pattern recognition and ignore the anatomical context in which pathologies develop. To narrow this gap, we introduce GRASP (Guided Representation Alignment for the Segmentation of Pathologies), a modular plug-and-play framework that enhances pathology segmentation models by leveraging existing anatomy segmentation models through pseudo-label integration and feature alignment.

Recommended citation: Li, K., Jaus, A., Kleesiek, J., & Stiefelhagen, R. (2025). GRASPing Anatomy to Improve Pathology Segmentation. In Z. Cui et al. (Eds.), MLMI 2025 Workshops, LNCS 16241, pp. 487–497. Springer Nature Switzerland.
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Is Visual in-Context Learning for Compositional Medical Tasks within Reach?

Published in IEEE/CVF International Conference on Computer Vision (ICCV), 2025

In this paper, we explore the potential of visual in-context learning to enable a single model to handle multiple tasks and adapt to new tasks during test time without re-training. Unlike previous approaches, our focus is on training in-context learners to adapt to sequences of tasks, rather than individual tasks. Our goal is to solve complex tasks that involve multiple intermediate steps using a single model, allowing users to define entire vision pipelines flexibly at test time.

Recommended citation: Reiß, S., Marinov, Z., Jaus, A., Seibold, C., Sarfraz, M. S., Rodner, E., & Stiefelhagen, R. (2025). Is Visual in-Context Learning for Compositional Medical Tasks within Reach? In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2025).
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Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets

Published in Medical Image Computing and Computer Assisted Intervention (MICCAI), 2026

Manually refining radiological segmentation masks is highly resource-intensive. To determine when this expert commitment is truly justified for the training of segmentation models, we investigate the relationship between label quality and model performance. Expanding beyond models trained directly for inference, we conduct the first study isolating the impact of label quality in pre-training datasets.

Recommended citation: Jaus, A., Marinov, Z., Reiß, S., Seibold, C., Wei, J., Kleesiek, J., & Stiefelhagen, R. (2026). Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets. Medical Image Computing and Computer Assisted Intervention (MICCAI).

Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation (Oral)

Published in IEEE International Symposium on Biomedical Imaging (ISBI), 2026

Traditional loss functions in medical image segmentation, such as Dice, often under-segment small lesions because their small relative volume contributes negligibly to the overall loss. To address this, instance-wise loss functions and metrics have been proposed to evaluate segmentation quality on a per-lesion basis. We introduce CC-DiceCE, a loss function based on the CC-Metrics framework, and compare it with the existing blob loss.

Recommended citation: Bouteille, L., Jaus, A., Kleesiek, J., Stiefelhagen, R., & Heine, L. (2026). Learning to Look Closer: A New Instance-Wise Loss for Small Cerebral Lesion Segmentation. In 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI). IEEE. https://doi.org/10.1109/ISBI61048.2026.11515682
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IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memory

Published in IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2026

Correcting errors in long-video understanding is disproportionately costly: existing multimodal pipelines produce opaque, end-to-end outputs that expose no intermediate state for inspection, forcing annotators to revisit raw video and reconstruct temporal logic from scratch. The core bottleneck is not generation quality alone, but the absence of a supervisory interface through which human effort can be proportional to the scope of each error.

Recommended citation: Kong, W., Wen, D., Peng, K., Schneider, D., Zhong, Z., Jaus, A., Marinov, Z., Wei, J., Liu, R., Zheng, J., Chen, Y., Qi, L., & Stiefelhagen, R. (2026). IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memory. IEEE International Conference on Systems, Man, and Cybernetics (SMC).
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IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction

Published in IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2026

We present IMPACT-HOI, a mixed-initiative framework for annotating egocentric procedural video by constructing structured event graphs for Human-Object Interactions (HOI), motivated by the need for high-quality structured supervision for learning robot manipulation from human demonstration. IMPACT-HOI frames this task as the incremental resolution of a partially specified, onset-anchored event state.

Recommended citation: Zhang, H., Wen, D., Peng, K., Schneider, D., Zhong, Z., Jaus, A., Marinov, Z., Wei, J., Liu, R., Zheng, J., Chen, Y., Zhang, Y., Luo, Y., Qi, L., & Stiefelhagen, R. (2026). IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction. IEEE International Conference on Systems, Man, and Cybernetics (SMC).
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IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning

Published in IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2026

Dense temporal annotation of procedural activity videos is vital for action understanding and embodied intelligence but remains labor-intensive due to reactive tools. Each correction is treated as an isolated edit, limiting reuse of information on annotator uncertainty and model reliability. We introduce IMPACT-Scribe, a correction-driven framework for dense labeling that uses each correction to improve future human–machine collaboration.

Recommended citation: Yin, Q., Wen, D., Peng, K., Schneider, D., Zhong, Z., Jaus, A., Marinov, Z., Wei, J., Liu, R., Zheng, J., Chen, Y., Zhang, C., Qi, L., & Stiefelhagen, R. (2026). IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning. IEEE International Conference on Systems, Man, and Cybernetics (SMC).
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The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT – Multitracer Multicenter Generalization

Published in MICCAI 2024 Challenge (autoPET3), 2026

We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body PET/CT under a compositional generalization setting. Training data comprised 1,014 [18F]-FDG PET/CT studies from the University Hospital Tübingen and 597 [18F]/[68Ga]-PSMA PET/CT studies from the LMU University Hospital Munich, constituting the largest publicly available annotated PSMA PET/CT dataset to date. The held-out test set of 200 studies covered four tracer–center combinations, two of which represented unseen compositional pairings.

Recommended citation: Dexl, J., Jeblick, K., Mittermeier, A., Schachtner, B., Stüber, A. T., Topalis, J., Rokuss, M., Isensee, F., Maier-Hein, K. H., Kalisch, H., Kleesiek, J., Seibold, C. M., Alasmawi, H., Chan, L. Y. L., Yuan, Y., Jaus, A., Stiefelhagen, R., et al. (2026). The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT – Multitracer Multicenter Generalization. MICCAI 2024 Challenge (autoPET3). arXiv:2605.05775.
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talks

teaching

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.