Maximilian Rokuss

Orcid: 0009-0004-4560-0760

According to our database1, Maximilian Rokuss authored at least 24 papers between 2023 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation.
CoRR, July, 2025

Segmenting the Inferior Alveolar Canal in CBCTs Volumes: The ToothFairy Challenge.
IEEE Trans. Medical Imaging, April, 2025

nnInteractive: Redefining 3D Promptable Segmentation.
CoRR, March, 2025

Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge.
CoRR, February, 2025

Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge.
CoRR, January, 2025

ScaleMAI: Accelerating the Development of Trusted Datasets and AI Models.
CoRR, January, 2025

LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

Abstract: Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting.
Proceedings of the Bildverarbeitung für die Medizin 2025, 2025

Abstract: Skeleton Recall Loss - Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures.
Proceedings of the Bildverarbeitung für die Medizin 2025, 2025

2024
RadioActive: 3D Radiological Interactive Segmentation Benchmark.
CoRR, 2024

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
CoRR, 2024

Data-Centric Strategies for Overcoming PET/CT Heterogeneity: Insights from the AutoPET III Lesion Segmentation Challenge.
CoRR, 2024

From FDG to PSMA: A Hitchhiker's Guide to Multitracer, Multicenter Lesion Segmentation in PET/CT Imaging.
CoRR, 2024


Mitigating False Predictions in Unreasonable Body Regions.
Proceedings of the Machine Learning in Medical Imaging - 15th International Workshop, 2024

Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 Workshops, 2024

Optimizing nnU-Net with OpenVINO for Fast CPU Inference in Abdominal Organ Segmentation.
Proceedings of the Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation, 2024

Efficient Cross-Modality Abdominal Organ Segmentation Using nnU-Net and MIND Descriptors.
Proceedings of the Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation, 2024

Efficient Pan-Cancer Lesion Segmentation from Partially Labeled Data with nnU-Net.
Proceedings of the Fast, Low-Resource, Accurate Robust Organ and Pan-cancer Segmentation, 2024

Enhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation Therapy.
Proceedings of the Head and Neck Tumor Segmentation for MR-Guided Applications, 2024

Scaling nnU-Net for CBCT Segmentation.
Proceedings of the Supervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data, 2024

Skeleton Recall Loss for Connectivity Conserving and Resource Efficient Segmentation of Thin Tubular Structures.
Proceedings of the Computer Vision - ECCV 2024, 2024

Segment Anything in Medical Images with nnUNet.
Proceedings of the Medical Image Segmentation Foundation Models. CVPR 2024 Challenge: Segment Anything in Medical Images on Laptop, 2024

2023
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model.
CoRR, 2023


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