Hotspot Detection of Coastal Regions

The increasing sea level and number of extreme weather events due to the climate change, affects and imperils the coastal regions and with it the people and logistics located in these regions. In order to detect threatening trends, we develop an AI based hotspot detection software applying unsupervised methods

ARTIFACT DETECTION IN SAR IMAGES WITH AUTOENCODER METHODS

Automated evaluation of Earth Observation data gains in importance due to their increasing number. Autoencder (AE) have been successfully applied to change detection on optical images. Here, we compare three different convolutional AE methods for change detection on time series of radar data

AI-based Evaluation of cardiac real-time MRI with congenital heart disease

Cardiac MRI scans are an important tool used by cardiologists. It can be utilized for diagnoses and assessment of vital parameters like the stroke volume or blood flow. The significant reduction in acquisition time that is achievable nowadays allows the recording of realtime 3D videos of the human heart. Such high frame rates yield very large amounts of data. To evaluate this data highly efficiently and with as little manual intervention as possible, it must be processed and interpreted automatically. We introduce the key challenges in such automated evaluations of realtime cardiac MRI. As patients are able breath freely during the examination, the effect of respiration on parameters such as blood flow and stroke volume can be investigated. However, this leads to the challenge of respiratory synchronization. Also, as the 3D representation of the heart is acquired slice by slice, the cardiac cycles in those slices must be synchronized. Another challenge is the physiological segmentation of the images. Especially for pathological hearts (e.g. univentricular hearts) this is a great challenge as high accuracy and automation are demanded. Furthermore, we present our workflow to tackle those challenges and show and discuss first results. One result is a breath- and cardiac cycle synchronized segmented univentricular heart in 4D (3D+time)

Automated Cardiac Realtime MRI Evaluation

We introduce our workflow to tackle automated evaluation of cardiac realtime MRI. The key approach is inspired by Active Learning and consists of N steps. First a limited amount of Training Data is annotated by staff with expert knowledge in the domain of pediatric cardiology. With this data we train a UNet using nnU-Net (Isensee, et. al). We then predict semantic labels with the trained model and use various techniques to judge the quality of each prediction. With that we are able to label each predicted segmentation with high or low quality. Predictions judged as low quality ones, are then presented to the domain experts and are manually corrected by them. Then, we can add those high quality labels to the training data set and start a new iteration by training the model. When the quality of predictions of an entire data set to be analyzed is high enough, we go on to synchronize the data set by assembling volumes of specific cardiac-respiration combinations based on the semantic segmentations. Finally, we are able to compute the stroke volume at different respiratory phases and compare them. The workflow explained above is deployed as a Plugin for the Software "3D Slicer"

Appropriate relaxation of non-pharmaceutical interventions minimizes the risk of a resurgence in SARS-CoV-2 infections in spite of the Delta variant

We analyze the relaxation of non-pharmaceutical interventions (NPIs) under an increasing number of vaccinations in Germany. For the spread of SARS-CoV-2 we employ a SIR-type model that accounts for age-dependence and includes realistic contact patterns between age groups. The implementation of NPIs occurs on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. We account for spatial heterogeneity and commuting activities in between regions in Germany, and the testing of commuters is considered as a further NPI. We include the ongoing vaccination process and analyze the effect of the B.1.617.2 (Delta) variant, which is considered to be \MJK{4}0\%-60\% more infectious then the currently dominant B.1.1.7 (Alpha) variant. We explore different opening scenarios under the ongoing vaccination process by assuming that local restrictions are either lifted in early July or August with or without continued wearing of masks and testing. Our results indicate that we can counteract the resurgence of SARS-CoV-2 despite the Delta variant with appropriate timing for the relaxation of NPIs. In all cases, however, school children are hit the hardest

Appropriate relaxation of non-pharmaceutical interventions minimizes the risk of a resurgence in SARS-CoV-2 infections in spite of the Delta variant

We analyze the relaxation of non-pharmaceutical interventions (NPIs) under an increasing number of vaccinations in Germany. For the spread of SARS-CoV-2 we employ a SIR-type model that accounts for age-dependence and includes realistic contact patterns between age groups. The implementation of NPIs occurs on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. We account for spatial heterogeneity and commuting activities in between regions in Germany, and the testing of commuters is considered as a further NPI. We include the ongoing vaccination process and analyze the effect of the B.1.617.2 (Delta) variant, which is considered to be \MJK{4}0\%-60\% more infectious then the currently dominant B.1.1.7 (Alpha) variant. We explore different opening scenarios under the ongoing vaccination process by assuming that local restrictions are either lifted in early July or August with or without continued wearing of masks and testing. Our results indicate that we can counteract the resurgence of SARS-CoV-2 despite the Delta variant with appropriate timing for the relaxation of NPIs. In all cases, however, school children are hit the hardest

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants

Background Despite the accelerating vaccination process, a large majority of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. Methods We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the counties are initialized randomly with incidences between 75-150 weekly new cases per 100,000 inhabitants (red zones) or below 10 (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20 % of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. Results We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, less strict lockdowns with the same commuter testing rate quickly and substantially lead to overall higher infection dynamics. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable

Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany

Background Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. Methods We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. Results We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics. Conclusions Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent

ARTIFACT DETECTION IN SAR IMAGES WITH AUTOENCODER METHODS

Automated evaluation of Earth Observation data gains in importance due to their increasing number. Autoencder (AE) have been successfully applied to change detection on optical images. Here, we compare three different convolutional AE methods for change detection on time series of radar data