@article{AnkenbrandLohrSchloetelburgetal.2021,
  author    = {Ankenbrand, Markus Johannes and Lohr, David and Schl{\"o}telburg, Wiebke and Reiter, Theresa and Wech, Tobias and Schreiber, Laura Maria},
  title     = {Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI},
  series = {Magnetic Resonance in Medicine},
  volume    = {86},
  journal   = {Magnetic Resonance in Medicine},
  number    = {4},
  doi       = {10.1002/mrm.28822},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-257604},
  pages     = {2179-2191},
  year      = {2021},
  abstract  = {Purpose Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pathologies is limited. Transfer learning addresses this challenge, but specific recommendations regarding type and amount of data required is lacking. In this study, we assess data requirements for transfer learning to experimental cardiac MRI at 7T where the segmentation task can be challenging. In addition, we provide guidelines, tools, and annotated data to enable transfer learning approaches by other researchers and clinicians. Methods A publicly available segmentation model was used to annotate a publicly available data set. This labeled data set was subsequently used to train a neural network for segmentation of left ventricle and myocardium in cardiac cine MRI. The network is used as starting point for transfer learning to 7T cine data of healthy volunteers (n = 22; 7873 images) by updating the pre-trained weights. Structured and random data subsets of different sizes were used to systematically assess data requirements for successful transfer learning. Results Inconsistencies in the publically available data set were corrected, labels created, and a neural network trained. On 7T cardiac cine images the model pre-trained on public imaging data, acquired at 1.5T and 3T, achieved DICE\(_{LV}\) = 0.835 and DICE\(_{MY}\) = 0.670. Transfer learning using 7T cine data and ImageNet weight initialization improved model performance to DICE\(_{LV}\) = 0.900 and DICE\(_{MY}\) = 0.791. Using only end-systolic and end-diastolic images reduced training data by 90\%, with no negative impact on segmentation performance (DICE\(_{LV}\) = 0.908, DICE\(_{MY}\) = 0.805). Conclusions This work demonstrates and quantifies the benefits of transfer learning for cardiac cine image segmentation. We provide practical guidelines for researchers planning transfer learning projects in cardiac MRI and make data, models, and code publicly available.},
  language  = {en}
}
@article{DetomasAltieriSchloetelburgetal.2021,
  author    = {Detomas, Mario and Altieri, Barbara and Schl{\"o}telburg, Wiebke and Appenzeller, Silke and Schlaffer, Sven and Coras, Roland and Schirbel, Andreas and Wild, Vanessa and Kroiss, Matthias and Sbiera, Silviu and Fassnacht, Martin and Deutschbein, Timo},
  title     = {Case Report: Consecutive Adrenal Cushing's Syndrome and Cushing's Disease in a Patient With Somatic CTNNB1, USP8, and NR3C1 Mutations},
  series = {Frontiers in Endocrinology},
  volume    = {12},
  journal   = {Frontiers in Endocrinology},
  issn      = {1664-2392},
  doi       = {10.3389/fendo.2021.731579},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-244596},
  year      = {2021},
  abstract  = {The occurrence of different subtypes of endogenous Cushing's syndrome (CS) in single individuals is extremely rare. We here present the case of a female patient who was successfully cured from adrenal CS 4 years before being diagnosed with Cushing's disease (CD). The patient was diagnosed at the age of 50 with ACTH-independent CS and a left-sided adrenal adenoma, in January 2015. After adrenalectomy and histopathological confirmation of a cortisol-producing adrenocortical adenoma, biochemical hypercortisolism and clinical symptoms significantly improved. However, starting from 2018, the patient again developed signs and symptoms of recurrent CS. Subsequent biochemical and radiological workup suggested the presence of ACTH-dependent CS along with a pituitary microadenoma. The patient underwent successful transsphenoidal adenomectomy, and both postoperative adrenal insufficiency and histopathological workup confirmed the diagnosis of CD. Exome sequencing excluded a causative germline mutation but showed somatic mutations of the β-catenin protein gene (CTNNB1) in the adrenal adenoma, and of both the ubiquitin specific peptidase 8 (USP8) and the glucocorticoid receptor (NR3C1) genes in the pituitary adenoma. In conclusion, our case illustrates that both ACTH-independent and ACTH-dependent CS may develop in a single individual even without evidence for a common genetic background.},
  language  = {en}
}