Zobrazeno 1 - 10
of 2 335
pro vyhledávání: '"Fechter AN"'
Autor:
Bhandary, Shrajan, Kuhn, Dejan, Babaiee, Zahra, Fechter, Tobias, Spohn, Simon K. B., Zamboglou, Constantinos, Grosu, Anca-Ligia, Grosu, Radu
Accurate segmentation of prostate tumours from PET images presents a formidable challenge in medical image analysis. Despite considerable work and improvement in delineating organs from CT and MR modalities, the existing standards do not transfer wel
Externí odkaz:
http://arxiv.org/abs/2407.10537
Autor:
Hartong, Nanna E., Sachpazidis, Ilias, Blanck, Oliver, Etzel, Lucas, Peeken, Jan C., Combs, Stephanie E., Urbach, Horst, Zaitsev, Maxim, Baltas, Dimos, Popp, Ilinca, Grosu, Anca-Ligia, Fechter, Tobias
Background: The aim of this study was to investigate the role of clinical, dosimetric and pretherapeutic magnetic resonance imaging (MRI) features for lesion-specific outcome prediction of stereotactic radiotherapy (SRT) in patients with brain metast
Externí odkaz:
http://arxiv.org/abs/2405.20825
Autor:
Sophia L. Bürkle, Dejan Kuhn, Tobias Fechter, Gianluca Radicioni, Nanna Hartong, Martin T. Freitag, Xuefeng Qiu, Efstratios Karagiannis, Anca-Ligia Grosu, Dimos Baltas, Constantinos Zamboglou, Simon K. B. Spohn
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-8 (2024)
Abstract This retrospective, multi-centered study aimed to improve high-quality radiation treatment (RT) planning workflows by training and testing a Convolutional Neural Network (CNN) to perform auto segmentations of organs at risk (OAR) for prostat
Externí odkaz:
https://doaj.org/article/29344628f458470ab9b902c7416d82cc
Autor:
Julius C. Holzschuh, Michael Mix, Martin T. Freitag, Tobias Hölscher, Anja Braune, Jörg Kotzerke, Alexis Vrachimis, Paul Doolan, Harun Ilhan, Ioana M. Marinescu, Simon K. B. Spohn, Tobias Fechter, Dejan Kuhn, Christian Gratzke, Radu Grosu, Anca-Ligia Grosu, C. Zamboglou
Publikováno v:
Radiation Oncology, Vol 19, Iss 1, Pp 1-8 (2024)
Abstract Purpose Convolutional Neural Networks (CNNs) have emerged as transformative tools in the field of radiation oncology, significantly advancing the precision of contouring practices. However, the adaptability of these algorithms across diverse
Externí odkaz:
https://doaj.org/article/3d3b7fb0056c447a9d725656b96a98f4
Autor:
Bhandary, Shrajan, Babaiee, Zahra, Kostyszyn, Dejan, Fechter, Tobias, Zamboglou, Constantinos, Grosu, Anca-Ligia, Grosu, Radu
Despite the success of convolutional neural networks for 3D medical-image segmentation, the architectures currently used are still not robust enough to the protocols of different scanners, and the variety of image properties they produce. Moreover, a
Externí odkaz:
http://arxiv.org/abs/2210.15949
Publikováno v:
Zeitschrift für Medizinische Physik, Vol 34, Iss 2, Pp 180-196 (2024)
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an e
Externí odkaz:
https://doaj.org/article/d749ee244e254d729dbf559b55705074
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an e
Externí odkaz:
http://arxiv.org/abs/2205.07516
Autor:
Kieninger, B., Fechter, R., Bäumler, W., Raab, D., Rath, A., Caplunik-Pratsch, A., Schmid, S., Müller, T., Schneider-Brachert, W., Eichner, A.
Publikováno v:
In Journal of Hospital Infection November 2024 153:39-46
Autor:
Schönhof, Raoul, Fechter, Manuel
Publikováno v:
Procedia CIRP Volume 91, 2020, Pages 433-438
Aiming for a higher economic efficiency in manufacturing, an increased degree of automation is a key enabler. However, assessing the technical feasibility of an automated assembly solution for a dedicated process is difficult and often determined by
Externí odkaz:
http://arxiv.org/abs/2202.04051
Autor:
Bhandary, Shrajan, Babaiee, Zahra, Kostyszyn, Dejan, Fechter, Tobias, Zamboglou, Constantinos, Grosu, Anca-Ligia, Grosu, Radu
Despite the great success of convolutional neural networks (CNN) in 3D medical image segmentation tasks, the methods currently in use are still not robust enough to the different protocols utilized by different scanners, and to the variety of image p
Externí odkaz:
http://arxiv.org/abs/2110.15664