Peer-reviewed conference publications

      • Kleinau A, Mo A, Müller-Sielaff J, Pijnenborg JMA, Lucas PJF, Oeltze-Jafra S. User-centered development of a clinical decision support system. Smartercare Workshop (2021), accepted manuscript


      • Mittenentzwei S, Sciarra A, Lüsebrink F, Aruci M, Ulbrich P, Schreiber F, Lemke A, Meuschke M, Preim B, Schreiber S, Oeltze-Jafra S. Visual analysis of brain lesion load in patients with cerebral small vessel disease. 56. Jahrestagung der Deutschen Gesellschaft für Neuroradiologie e. V.. Clin Neuroradiol. (2021);31:1-73 (Abstr 57)


      • Aruci M, Dünnwald M, Schreiber F, Sciarra A, Maass A, Schreiber S, Oeltze-Jafra S. Challenging cases for WMH segmentation comparatively processed by seven automated methods. 56. Jahrestagung der Deutschen Gesellschaft für Neuroradiologie e. V.. Clin Neuroradiol. (2021);31:1-73 (Abstr 208)


      • Dünnwald M, Betts MJ, Düzel E, Oeltze-Jafra S. Localization of the Locus Coeruleus in MRI via Coordinate Regression. In: Palm C., Deserno T.M., Handels H., Maier A., Maier-Hein K., Tolxdorff T. (eds) Bildverarbeitung für die Medizin (2021). Informatik aktuell. Springer Vieweg, Wiesbaden.


      • Müller J, Cypko MA  Oeser A, Stöhr M, Zebralla V, Schreiber S, Wiegand S, Dietz A, Oeltze-Jafra S. Visual assistance in clinical decision support. EuroVis 2021 - 23rd Eurographics Conference on Visualization (2021), Dirk Bartz Prize 2021 - Eurographics Association, 2021.


      • Chatterjee S, Sarasaen C, Sciarra A, Breitkopf M, Oeltze-Jafra S, Nürnberger A, Speck, O. Going beyond the image space: under-sampled MRI reconstruction directly in the k-space using a complexvalued residual neural network. In: 2021 ISMRM & SMRT Annual Meeting & Exhibition. May 2021, p. 1757.


      • Chatterjee S, Sciarra ADünnwald M, Agrawal S, Tummala P, Setlur D, Kalra A, Jauhari A, Oeltze-Jafra S, Speck O, Nürnberger A. Unsupervised reconstruction based anomaly detection using a Variational Auto Encoder.  In: 2021 ISMRM & SMRT Annual Meeting & Exhibition. May 2021, p. 2399.


      • Nath V, Pizzolato M, Palombo M, Gyori N, Schilling K, Hansen C, Yang Q, Kanakaraj P, Landman B, Chatterjee S, Sciarra ADünnwald MOeltze-Jafra S, Nürnberger A, Speck O, Pieciak T, Baranek M, Bartocha K, Ciupek D, Hutter J. Resolving to super resolution multi-dimensional diffusion imaging (Super-MUDI). In: 2021 ISMRM & SMRT Annual Meeting & Exhibition. May 2021, p. 0103.


      • Chatterjee S, Sciarra ADünnwald M, Mushunuri RV, Podishetti R, Rao RN, Gopinath GD, Oeltze-Jafra S, Speck O, Nürnberger A. ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning. In: 2021 29th European Signal Processing Conference (EUSIPCO). IEEE. Aug 2021.


      • Steinhauer N, Hörbrügger M, Braun A, Tütting T, Oeltze-Jafra SMüller J. Comprehensive Visualization of Longitudinal Patient Data for the Dermatological Oncological Tumor Board. EuroVIS 2020 - Short Papers, The Eurographics Association, 2020 paper presentation (starting 1:05:50)


      • Hämmerer D, Hopkins A, Ludwig M, Yi YJ, Lüsebrink F, Liu K, Femminella GD, Betts MJ, Callaghan MF, Howard RJ, Düzel E. Functional indicators of a decline in the noradrenergic locus coeruleus in ageing: Neuroimaging / Optimal neuroimaging measures for tracking disease progression. AAIC 2020 Virtual Conference. Alzheimer’s Dement. 2020;16(Suppl. 5):e044582. doi: 10.1002/alz.044582


      • Ludwig M, Hämmerer D, Lüsebrink F, Düzel D. Interrogating the role of the noradrenergic locus coeruleus in memory encoding in aging: Neuroimaging / Optimal neuroimaging measures for early detection. AAIC 2020 Virtual Conference. Alzheimer’s Dement. 2020;16(Suppl. 5):e044039. doi: 10.1002/alz.044039


      • Iamshchinina P, Kaiser D, Yakupov R, Haenelt D, Sciarra A, Mattern H, Duezel E, Speck O, Weiskopf N, Cichy RM; Perceived and mentally rotated contents are differentially represented in cortical layers of V1. Vision Sciences Society Annual Meeting 2020. Journal of Vision 2020;20(11):766.


      • Sciarra A, Dünnwald M, Chatterjee S, Speck O, Oeltze-Jafta S. Classification of Motion Corrupted Brain MR Images using Deep Learning Techniques. ESMRMB 2020, Online. Magn Reson Mater Phy (2020) 33(Suppl 1):S03.09.


      • Lüsebrink F, Mattern H, Yakupov R, Oeltze-Jafra S, Speck O. The human phantom: Comprehensive ultrahigh resolution whole brain in vivo single subject dataset. ISMRM & SMRT Virtual Conference & Exhibition 2020. Proc Intl Soc Mag Reson Med 28 (2020): . doi: 10.24352/UB.OVGU-2020-145, oral presentationMagna cum laude Award


      • Mattern H, Knoll M, Lüsebrink F, Speck O. Chemical shift based prospective k-space anonymization. Magn Reson Med. 2020; 85: 962– 969. Magna cum laude Award at the ISMRM & SMRT Virtual Conference & Exhibition 2020.


      • Sciarra A, Chatterjee S, Dünnwald M, Speck O, Oeltze-Jafra S. Evaluation of Deep Learning Techniques for Motion Artifacts Removal. ISMRM Congress 2020. Proc Intl Soc Mag Reson Med 28 (2020):P3370


      • Sciarra ADünnwald M, Mattern H, Speck O, Oeltze-Jafra S. Super-Resolution with Conditional-GAN for MR Brain Images. ISMRM Congress 2020. Proc Intl Soc Mag Reson Med 28 (2020):P3540 


      • Mattern H, Sciarra ADünnwald M, Chatterjee S, Müller U, Oeltze-Jafra S, Speck O. Contrast prediction-based regularization for iterative reconstructions (PROSIT). ISMRM Congress 2020. Proc Intl Soc Mag Reson Med 28 (2020):P3462


      • Tung Y-H, Godenschweger F, In M-H, Sciarra A, Speck O. Simultaneously multi-slice VAT-DIADEM at ultra-high field. ISMRM Congress 2020. Proc Intl Soc Mag Reson Med 28 (2020):P4370


      • Dünnwald M, Betts MJ, Sciarra A, Düzel E, Oeltze-Jafra S. Automated Segmentation of the Locus Coeruleus from Neuromelanin-sensitive 3T MRI using Deep Convolutional Networks. In: Tolxdorff T, Deserno T, Handels H, Maier A, Maier-Hein K, Palm C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden. poster presentation 


      • Hille G, Dünnwald M, Becker M, Steffen J, Saalfeld S, Tönnies K. Segmentation of Vertebral Metastases in MR Imaging using an U-Net like Deep Neural Network. In: Handels H, Deserno T, Maier A, Maier-Hein K, Palm C, Tolxdorff T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden.



      • Lüsebrink F, Mattern H, Oeltze-Jafra S, Speck O. Beyond high resolution: Denoising during image reconstruction to improve image quality. ESMRMB Congress 2019, Magn Reson Mater Phy (2019) 32(Suppl 1):S271-272. doi: 10.1007/s10334-019-00755-1.


      • Dubost F, Dünnwald M, Huff D, Scheumann V, Schreiber F, Vernooij M, Niessen W, Skalej M, Schreiber S, Oeltze-Jafra S, de Bruijne M. Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI across Sites. Workshop MLCN 2019. In: Zhou L. et al. (eds) OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging. OR 2.0 2019, MLCN 2019. Lecture Notes in Computer Science, vol 11796. Springer, Cham.


      • Müller J, Zebralla V, Wiegand S, Oeltze-Jafra S. Interactive Visual Analysis of Patient-Reported Outcomes for Improved Cancer Aftercare. EuroVis Workshop on Visual Analytics (EuroVA), p. 5 pages, 2019.


  • Abstract:

The monitoring and planning of cancer aftercare are commonly based on clinical, physiological and caregiver-reported outcome measures. More recently, patient-reported outcome (PRO) measures, capturing social, psychological, and financial aspects, are gaining attention in the course of establishing a patient-centered healthcare system. PROs are acquired during regular aftercare consultations where patients are asked to fill in questionnaires. We present an interactive visual analysis (IVA) approach to investigating PROs. The approach is applied in clinical routine during the aftercare consultation to assess the development of the particular patient, to compare this development to those of similar patients, and to detect trends that may require an adaptation of the aftercare strategy. Furthermore, the approach is employed in clinical research to identify groups of similarly developing patients and risk factors for poor outcomes, as well as to visually compare patient groups. We demonstrate the IVA approach in analyzing PROs of 1025 head and neck cancer patients. In an evaluation with 20 clinicians, we assessed the usefulness and usability of a prototypical implementation.

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