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000299862 0247_ $$2doi$$a10.48550/ARXIV.2502.20985
000299862 037__ $$aDKFZ-2025-00592
000299862 1001_ $$0P:(DE-He78)936ebccdc011e3efd9ffc0bdcc2d8379$$aRokuss, Maximilian$$b0$$eFirst author$$udkfz
000299862 245__ $$aLesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging
000299862 260__ $$barXiv$$c2025
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000299862 3367_ $$2BibTeX$$aARTICLE
000299862 3367_ $$2DataCite$$aOutput Types/Working Paper
000299862 500__ $$aLicense: CC BY-NC-SA 4.0
000299862 520__ $$aAbstract: This archive contains the Lesion Dataset with Synthetic Follow-ups, which provides original and synthetic second-timepoint images with annotations as part of the LesionLocator paper, a framework for zero-shot universal tumor segmentation and tracking in 3D whole-body imaging. The dataset is approximately 700 GB in size and contains around 5,200 images. It is designed to support research in lesion tracking, segmentation, and progression analysis. Lesions are annotated with instance-based labels, ensuring consistent lesion identification across both timepoints. The dataset includes images sourced from multiple publicly available datasets, covering a variety of lesion types and anatomical regions. Due to constraints related to image size, quality, or licensing, not all images were included in the final dataset. This dataset is particularly well suited for pretraining or for use in combination with real longitudinal imaging data to improve model generalization. For longitudinal tracking tasks, we recommend introducing image misalignment through data augmentation, such as cropping one timepoint, to better simulate real-world conditions. A more detailed description of the dataset is available in the paper or the archive, downloadable via the "Fulltext" link at the bottom of the page.
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000299862 588__ $$aDataset connected to DataCite
000299862 650_7 $$2Other$$aComputer Vision and Pattern Recognition (cs.CV)
000299862 650_7 $$2Other$$aArtificial Intelligence (cs.AI)
000299862 650_7 $$2Other$$aFOS: Computer and information sciences
000299862 7001_ $$0P:(DE-He78)11abdd498226fde3e6b67ba107bf4e83$$aKirchhoff, Yannick$$b1$$udkfz
000299862 7001_ $$0P:(DE-He78)be4d0bbacce2cd31fc4287ad4e66edd1$$aAkbal, Seval$$b2$$udkfz
000299862 7001_ $$0P:(DE-He78)6bfeb1f4178c095061573c14780e1377$$aKovacs, Balint$$b3$$udkfz
000299862 7001_ $$0P:(DE-He78)e2245a7841121bee37e036c68b55ec94$$aRoy, Saikat$$b4$$udkfz
000299862 7001_ $$0P:(DE-He78)1bf529d39d90e30ceb901da6e5816185$$aUlrich, Constantin$$b5$$udkfz
000299862 7001_ $$0P:(DE-He78)4412d586f86ca57943732a2b9318c44f$$aWald, Tassilo$$b6$$udkfz
000299862 7001_ $$0P:(DE-He78)d7135c1486ffd923f71735d40a3d7a0c$$aRotkopf, Lukas T.$$b7$$udkfz
000299862 7001_ $$0P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec$$aSchlemmer, Heinz-Peter$$b8$$udkfz
000299862 7001_ $$0P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3$$aMaier-Hein, Klaus$$b9$$eLast author$$udkfz
000299862 773__ $$ahttps://doi.org/10.48550/arXiv.2502.20985$$tarXiv$$y2025
000299862 8564_ $$uhttps://doi.dkfz.de/10.6097/DKFZ/IR/E230/20250324_1.zip
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