@article{WoznickiLaquaBleyetal.2022,
  author    = {Woznicki, Piotr and Laqua, Fabian and Bley, Thorsten and Baeßler, Bettina},
  title     = {AutoRadiomics: a framework for reproducible radiomics research},
  series = {Frontiers in Radiology},
  volume    = {2},
  journal   = {Frontiers in Radiology},
  issn      = {2673-8740},
  doi       = {10.3389/fradi.2022.919133},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-284813},
  year      = {2022},
  abstract  = {Purpose Machine learning based on radiomics features has seen huge success in a variety of clinical applications. However, the need for standardization and reproducibility has been increasingly recognized as a necessary step for future clinical translation. We developed a novel, intuitive open-source framework to facilitate all data analysis steps of a radiomics workflow in an easy and reproducible manner and evaluated it by reproducing classification results in eight available open-source datasets from different clinical entities. Methods The framework performs image preprocessing, feature extraction, feature selection, modeling, and model evaluation, and can automatically choose the optimal parameters for a given task. All analysis steps can be reproduced with a web application, which offers an interactive user interface and does not require programming skills. We evaluated our method in seven different clinical applications using eight public datasets: six datasets from the recently published WORC database, and two prostate MRI datasets—Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-UCLA) and PROSTATEx. Results In the analyzed datasets, AutoRadiomics successfully created and optimized models using radiomics features. For WORC datasets, we achieved AUCs ranging from 0.56 for lung melanoma metastases detection to 0.93 for liposarcoma detection and thereby managed to replicate the previously reported results. No significant overfitting between training and test sets was observed. For the prostate cancer detection task, results were better in the PROSTATEx dataset (AUC = 0.73 for prostate and 0.72 for lesion mask) than in the Prostate-UCLA dataset (AUC 0.61 for prostate and 0.65 for lesion mask), with external validation results varying from AUC = 0.51 to AUC = 0.77. Conclusion AutoRadiomics is a robust tool for radiomic studies, which can be used as a comprehensive solution, one of the analysis steps, or an exploratory tool. Its wide applicability was confirmed by the results obtained in the diverse analyzed datasets. The framework, as well as code for this analysis, are publicly available under https://github.com/pwoznicki/AutoRadiomics.},
  language  = {en}
}
@article{AngayFriedrichPinneckeretal.2018,
  author    = {Angay, Oguzhan and Friedrich, Mike and Pinnecker, J{\"u}rgen and Hintzsche, Henning and Stopper, Helga and Hempel, Klaus and Heinze, Katrin G.},
  title     = {Image-based modeling and scoring of Howell-Jolly Bodies in human erythrocytes},
  series = {Cytometry Part A},
  volume    = {93},
  journal   = {Cytometry Part A},
  doi       = {10.1002/cyto.a.23123},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-221140},
  pages     = {305-313},
  year      = {2018},
  abstract  = {The spleen selectively removes cells with intracellular inclusions, for example, detached nuclear fragments in circulating erythrocytes, called Howell-Jolly Bodies (HJBs). With absent or deficient splenic function HJBs appear in the peripheral blood and can be used as a simple and non-invasive risk-indicator for fulminant potentially life-threatening infection after spleenectomy. However, it is still under debate whether counting of the rare HJBs is a reliable measure of splenic function. Investigating HJBs in premature erythrocytes from patients during radioiodine therapy gives about 10 thousand times higher HJB counts than in blood smears. However, we show that there is still the risk of false-positive results by unspecific nuclear remnants in the prepared samples that do not originate from HJBs, but from cell debris residing above or below the cell. Therefore, we present a method to improve accuracy of image-based tests that can be performed even in non-specialized medical institutions. We show how to selectively label HJB-like clusters in human blood samples and how to only count those that are undoubtedly inside the cell. We found a "critical distance" dcrit referring to a relative HJB-Cell distance that true HJBs do not exceed. To rule out false-positive counts we present a simple inside-outside-rule based on dcrit—a robust threshold that can be easily assessed by combining conventional 2D imaging and straight-forward image analysis. Besides data based on fluorescence imaging, simulations of randomly distributed HJB-like objects on realistically modelled cell objects demonstrate the risk and impact of biased counting in conventional analysis. © 2017 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.},
  language  = {en}
}
@article{KollmannsbergerKerschnitzkiReppetal.2017,
  author    = {Kollmannsberger, Philip and Kerschnitzki, Michael and Repp, Felix and Wagermaier, Wolfgang and Weinkamer, Richard and Fratzl, Peter},
  title     = {The small world of osteocytes: connectomics of the lacuno-canalicular network in bone},
  series = {New Journal of Physics},
  volume    = {19},
  journal   = {New Journal of Physics},
  number    = {073019},
  doi       = {10.1088/1367-2630/aa764b},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-170662},
  year      = {2017},
  abstract  = {Osteocytes and their cell processes reside in a large, interconnected network of voids pervading the mineralized bone matrix of most vertebrates. This osteocyte lacuno-canalicular network (OLCN) is believed to play important roles in mechanosensing, mineral homeostasis, and for the mechanical properties of bone. While the extracellular matrix structure of bone is extensively studied on ultrastructural and macroscopic scales, there is a lack of quantitative knowledge on how the cellular network is organized. Using a recently introduced imaging and quantification approach, we analyze the OLCN in different bone types from mouse and sheep that exhibit different degrees of structural organization not only of the cell network but also of the fibrous matrix deposited by the cells. We define a number of robust, quantitative measures that are derived from the theory of complex networks. These measures enable us to gain insights into how efficient the network is organized with regard to intercellular transport and communication. Our analysis shows that the cell network in regularly organized, slow-growing bone tissue from sheep is less connected, but more efficiently organized compared to irregular and fast-growing bone tissue from mice. On the level of statistical topological properties (edges per node, edge length and degree distribution), both network types are indistinguishable, highlighting that despite pronounced differences at the tissue level, the topological architecture of the osteocyte canalicular network at the subcellular level may be independent of species and bone type. Our results suggest a universal mechanism underlying the self-organization of individual cells into a large, interconnected network during bone formation and mineralization.},
  language  = {en}
}