@article{AnkenbrandShainbergHocketal.2021,
  author    = {Ankenbrand, Markus J. and Shainberg, Liliia and Hock, Michael and Lohr, David and Schreiber, Laura M.},
  title     = {Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI},
  series = {BMC Medical Imaging},
  volume    = {21},
  journal   = {BMC Medical Imaging},
  number    = {1},
  doi       = {10.1186/s12880-021-00551-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259169},
  pages     = {27},
  year      = {2021},
  abstract  = {Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.},
  language  = {en}
}
@article{HockTerekhovStefanescuetal.2021,
  author    = {Hock, Michael and Terekhov, Maxim and Stefanescu, Maria Roxana and Lohr, David and Herz, Stefan and Reiter, Theresa and Ankenbrand, Markus and Kosmala, Aleksander and Gassenmaier, Tobias and Juchem, Christoph and Schreiber, Laura Maria},
  title     = {B\(_{0}\) shimming of the human heart at 7T},
  series = {Magnetic Resonance in Medicine},
  volume    = {85},
  journal   = {Magnetic Resonance in Medicine},
  number    = {1},
  doi       = {10.1002/mrm.28423},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-218096},
  pages     = {182 -- 196},
  year      = {2021},
  abstract  = {Purpose Inhomogeneities of the static magnetic B\(_{0}\) field are a major limiting factor in cardiac MRI at ultrahigh field (≥ 7T), as they result in signal loss and image distortions. Different magnetic susceptibilities of the myocardium and surrounding tissue in combination with cardiac motion lead to strong spatio-temporal B\(_{0}\)-field inhomogeneities, and their homogenization (B0 shimming) is a prerequisite. Limitations of state-of-the-art shimming are described, regional B\(_{0}\) variations are measured, and a methodology for spherical harmonics shimming of the B\(_{0}\) field within the human myocardium is proposed. Methods The spatial B\(_{0}\)-field distribution in the heart was analyzed as well as temporal B\(_{0}\)-field variations in the myocardium over the cardiac cycle. Different shim region-of-interest selections were compared, and hardware limitations of spherical harmonics B\(_{0}\) shimming were evaluated by calibration-based B0-field modeling. The role of third-order spherical harmonics terms was analyzed as well as potential benefits from cardiac phase-specific shimming. Results The strongest B\(_{0}\)-field inhomogeneities were observed in localized spots within the left-ventricular and right-ventricular myocardium and varied between systolic and diastolic cardiac phases. An anatomy-driven shim region-of-interest selection allowed for improved B\(_{0}\)-field homogeneity compared with a standard shim region-of-interest cuboid. Third-order spherical harmonics terms were demonstrated to be beneficial for shimming of these myocardial B\(_{0}\)-field inhomogeneities. Initial results from the in vivo implementation of a potential shim strategy were obtained. Simulated cardiac phase-specific shimming was performed, and a shim term-by-term analysis revealed periodic variations of required currents. Conclusion Challenges in state-of-the-art B\(_{0}\) shimming of the human heart at 7 T were described. Cardiac phase-specific shimming strategies were found to be superior to vendor-supplied shimming.},
  language  = {en}
}