Grid structures are common in high-throughput assays to parallelize experiments in biochemical or biological experiments. Manual analysis of grid images is laborious, time-consuming, expensive, and critical in terms of reproducibility. However, it is still common to do such analysis manually, as there is no standardized software for automated analysis. In this paper, we introduce a generic method to automatically detect grid structures in images and to perform flexible spot-wise analysis after successful grid detection. The deep learning-based approach of the grid structure detection allows being flexible concerning different grid types. The combination with a robust parameter estimation algorithm lowers the requirements of the detection quality and thus enhances robustness. Further, the method conducts semi-automated grid detection if a fully automated processing fails. An open-source software tool Grid Screener that implements the proposed methods is provided as a ready-for-use tool for researchers. The usability is demonstrated by taking different criteria into account, which are important for a successful application. We present the benefits of our proposed tool Grid Screener utilizing three different grid types in the context of high-throughput screening to show our contribution towards further lab automation. Our tool performs much faster than manual analysis, while maintaining or even enhancing accuracy.
Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehensive tool for assistance which is time- consuming, burdensome, and not intuitive. Using the here presented modular Karlsruhe Image Data Annotation (KaIDA) tool, for the first time assisted annotation in various image processing tasks is possible to support users during this process. It aims to simplify annotation, increase user efficiency, enhance annotation quality, and provide additional useful annotation-related functionalities. KaIDA is available open-source at https://git.scc.kit.edu/sc1357/kaida.
Background: The low-lying electric dipole strength provides insights on the parameters of the nuclear equation of state via its connection with the pygmy dipole resonance and nuclear neutron skin thickness.Purpose: Complement the systematic of the pygmy dipole resonance and first study its behavior across the N = 28 neutron shell closure.Methods: Photon-scattering cross sections of states of 50,54 Cr were measured up to an excitation energy of 9.7 MeV via the nuclear resonance fluorescence method using γ-ray beams from bremsstrahlung and Compton backscattering.Results: Transitions strengths, spin and parity quantum number and average branching ratios for 55 excited states, 44 of which were observed for the first time, were determined. The comparison between the total observed strengths of the isotopes 50,52,54 Cr shows a significant increase above the shell closure. Conclusions:The evolution of the pygmy dipole resonance is heavily influenced by the shell structure.
In this work, the growth and conductivity of semipolar AlxGa1-xN:Si with (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22) orientation is investigated. AlxGa1-xN:Si (x = 0.60 ± 0.03 and x = 0.80 ± 0.02) layers were grown with different SiH4 partial pressures and the electrical properties were determined using Hall measurements at room temperature. The aluminum mole fraction was measured by wavelength dispersive X-ray spectroscopy and X-ray diffraction and the Si-concentration was measured by wavelength dispersive X-ray spectroscopy and secondary ion mass spectroscopy. Layer resistivities as low as 0.024 Ω cm for x = 0.6 and 0.042 Ω cm for x = 0.8 were achieved. For both aluminum mole fractions the resistivity exhibits a minimum with increasing Si concentration which can be explained by compensation due to formation of cation vacancy complexes at high doping levels. The onset of self-compensation occurs at larger estimated Si concentrations for larger Al contents.
Supervised deep learning approaches for automated diagnosis support require datasets annotated by experts. Intra-annotator variability of a single annotator and inter-annotator variability between annotators can affect the quality of the diagnosis support. As medical experts will always differ in annotation details, quantitative studies concerning the annotation quality are of particular interest. A consistent and noise-free annotation of large-scale datasets by, for example, dermatologists or pathologists is a current challenge. In this paper, we categorize annotation noise in image segmentation tasks, present methods to simulate annotation noise, and examine the impact on the segmentation quality. Two novel automated methods to identify intra-annotator and inter-annotator inconsistencies based on uncertainty-aware deep neural networks are proposed. We demonstrate the benefits of our inspection methods such as focused reinspection of noisy annotations or the detection of generally different annotation styles using the biomedical ISIC 2017 Melanoma image segmentation dataset.
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