Zusammenfassung
With the introduction of whole slide imaging (WSI) systems, several digital pathology applications have emerged. Despite all benefits, lacking appropriate infrastructure to process proprietary WSI file formats for remote diagnosis and annotation is a constraint for widespread application of digital pathology. The joint imaging platform (JIP) already includes a wide range of solutions for digital medical image processing, mainly focused on radiology. We extend the infrastructure in the JIP for accessing, storage, remote analysis and deep learningbased processing of pathological data. By converting proprietaryWSI file formats into the DICOM standard, we enable the linkage of radiology and pathology on the JIP and show potential applications in current research studies.
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Literatur
Soarce J, Aberle DR, Elimam D, Lawvere S, Tawfik O, Wallace DW. Integrating pathology and radiology disciplines: an emerging opportunity? BMC Med. 2012;10(100).
Jungmann F, Kaissis GA, Ziegelmayer S, Harder F, Schilling C, Yen HY et al. Prediction of tumor cellularity in resectable PDAC from preoperative computed tomography imaging. Cancers (Basel). 2021;13(9).
Herrmann MD, Clunie DA, Fedorov A, Doyle SW, Pieper S, Klepeis V et al. Implementing the DICOM standard for digital pathology. J Pathol Inform. 2018;9(1):37.
Scherer J, Nolden M, Kleesiek J, Metzger J, Kades K, Schneider V et al. Joint imaging platform for federated clinical data analytics. JCO Clin Cancer Inform. 2020;4:1027–38.
Clunie DA. DICOM format and protocol standardization: a core requirement for digital pathology success. Toxicol Pathol. 2021;49(4). PMID: 33063645:738–49.
DICOM standards committee wg2. DICOM supplement 145-whole slide microscopic image IOD and SOP classes.
Fedorov A, Longabaugh WJ, Pot D, Clunie DA, Pieper S, Aerts HJ et al. NCI imaging data commons. Cancer Res. 2021;81(16):4188–93.
Bejnordi BE, Veta M, Diest PJ van, Ginneken B van, Karssemeijer N, Litjens G et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastasis in women with breast cancer. JAMA. 2017;318(22):2199–210.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. 2015.
Berman AG, Orchard WR, Gehrung M, Markowetz F. PathML: a unified framework for whole-slide image analysis with deep learning. medRxiv. 2021.
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Fischer, M. et al. (2022). DICOM Whole Slide Imaging for Computational Pathology Research in Kaapana and the Joint Imaging Platform. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_58
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DOI: https://doi.org/10.1007/978-3-658-36932-3_58
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