@article{DotterweichTowerBrandletal.2016,
  author    = {Dotterweich, Julia and Tower, Robert J. and Brandl, Andreas and M{\"u}ller, Marc and Hofbauer, Lorenz C. and Beilhack, Andreas and Ebert, Regina and Gl{\"u}er, Claus C. and Tiwari, Sanjay and Sch{\"u}tze, Norbert and Jakob, Franz},
  title     = {The KISS1 Receptor as an In Vivo Microenvironment Imaging Biomarker of Multiple Myeloma Bone Disease},
  series = {PLoS One},
  volume    = {11},
  journal   = {PLoS One},
  number    = {5},
  doi       = {10.1371/journal.pone.0155087},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-146960},
  pages     = {e0155087},
  year      = {2016},
  abstract  = {Multiple myeloma is one of the most common hematological diseases and is characterized by an aberrant proliferation of plasma cells within the bone marrow. As a result of crosstalk between cancer cells and the bone microenvironment, bone homeostasis is disrupted leading to osteolytic lesions and poor prognosis. Current diagnostic strategies for myeloma typically rely on detection of excess monoclonal immunoglobulins or light chains in the urine or serum. However, these strategies fail to localize the sites of malignancies. In this study we sought to identify novel biomarkers of myeloma bone disease which could target the malignant cells and/or the surrounding cells of the tumor microenvironment. From these studies, the KISS1 receptor (KISS1R), a G-protein-coupled receptor known to play a role in the regulation of endocrine functions, was identified as a target gene that was upregulated on mesenchymal stem cells (MSCs) and osteoprogenitor cells (OPCs) when co-cultured with myeloma cells. To determine the potential of this receptor as a biomarker, in vitro and in vivo studies were performed with the KISS1R ligand, kisspeptin, conjugated with a fluorescent dye. In vitro microscopy showed binding of fluorescently-labeled kisspeptin to both myeloma cells as well as MSCs under direct co-culture conditions. Next, conjugated kisspeptin was injected into immune-competent mice containing myeloma bone lesions. Tumor-burdened limbs showed increased peak fluorescence compared to contralateral controls. These data suggest the utility of the KISS1R as a novel biomarker for multiple myeloma, capable of targeting both tumor cells and host cells of the tumor microenvironment.},
  language  = {en}
}
@article{StebaniBlaimerZableretal.2023,
  author    = {Stebani, Jannik and Blaimer, Martin and Zabler, Simon and Neun, Tilmann and Pelt, Dani{\"e}l M. and Rak, Kristen},
  title     = {Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework},
  series = {Scientific Reports},
  volume    = {13},
  journal   = {Scientific Reports},
  doi       = {10.1038/s41598-023-45466-9},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357411},
  year      = {2023},
  abstract  = {Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic segmentation of the inner ear and anatomical landmark detection of helicotrema, oval and round window. A fully automated pipeline with a single, dual-headed volumetric 3D U-Net was implemented, trained and evaluated using manually labeled in-house datasets from cadaveric specimen (N = 43) and clinical practice (N = 9). The model robustness was further evaluated on three independent open-source datasets (N = 23 + 7 + 17 scans) consisting of cadaveric specimen scans. For the in-house datasets, Dice scores of 0.97 and 0.94, intersection-over-union scores of 0.94 and 0.89 and average Hausdorf distances of 0.065 and 0.14 voxel units were achieved. The landmark localization task was performed automatically with an average localization error of 3.3 and 5.2 voxel units. A robust, albeit reduced performance could be attained for the catalogue of three open-source datasets. Results of the ablation studies with 43 mono-parametric variations of the basal architecture and training protocol provided task-optimal parameters for both categories. Ablation studies against single-task variants of the basal architecture showed a clear performance beneft of coupling landmark localization with segmentation and a dataset-dependent performance impact on segmentation ability.},
  language  = {en}
}