The Growth Mechanism of Lithium Dendrites and its Coupling to Mechanical Stress

Operando high-resolution light microscopy with extended depth of field is used to observe large regions of an electrode during electrodeposition of lithium. The analysis of the morphology of the evolving deposit reveals that besides electrochemistry, mechanics and crystalline defects play a major role in the growth mechanism. Based on the findings, a growth mechanism is proposed that involves the diffusion of lithium atoms from the lithium surface into grain boundaries and the insertion into crystalline defects in the metal. Crystalline defects are a result of plastic deformation and hence mechanical stimulation augments the insertion of lithium

Switching from Lithium to Sodium—an Operando Investigation of an FePO4_{4} Electrode by Mechanical Measurements and Electron Microscopy

Many physical and chemical properties of Na$^{+}$ are very similar to those of Li$^{+}$, and therefore, some electrode materials for lithium-ion batteries can also work with sodium ions. As the Na$^{+}$ ion is larger than Li$^{+}$, the strains in the host lattice are larger, which can cause deviations in the electrochemical reactions. Herein, mechanical stresses are compared, which are measured by the in situ substrate curvature method during (de)lithiation/(de)sodiation of an FePO$_{4}$ electrode. The (de)lithiation and (de)sodiation experiments are performed on the same electrode. According to the change of the lattice parameters, during electrode operation, NaxFePO$_{4}$ particles experience a volume change that is 2.6 times larger than that of LixFePO$_{4}$. In the measurements, the composite electrode exhibits a change of the stress amplitude between operation with Li and Na by roughly one order of magnitude for 0 < x < 1. Compared with Li$^{+}$, the mechanical stress evolution during extraction and insertion of Na$^{+}$ is highly asymmetric. The observed asymmetry in the electrochemical and the mechanical data may be explained by the different energies that are required to move an intermediary amorphous phase away from or toward the crystalline sodium-rich regions during the (de)sodiation of NaFePO$_{4}$

Similarities in Lithium Growth at Vastly Different Rates

Lithium electrodeposition is important for lithium metal batteries and is presently a safety and reliability concern for the lithium-ion technology. In the literature, many models for the growth of dendrites can be found and a strong dependence on deposition rate is expected. To elucidate the process of the lithium deposition, operando light microscopy at the physical resolution limit of light was performed at rates varying by more than three orders of magnitude. The results show different growth regimes depending on the rate, and where needles, bushes, or accelerated bushes dominate the deposition. All these deposits are based on small crystalline needles and flakes. Little evidence for concentration gradient driven deposition was found. At the highest rate, the electrolyte ionically depletes, but the deposition continues by non-directional bush growth mainly from their insides. An important step at all rates is the insertion into defects in the crystalline lithium

The growth mechanism of lithium dendrites and its coupling to mechanical stress

Operando high-resolution light microscopy with extended depth of field is used to observe large regions of an electrode during electrodeposition of lithium. The analysis of the morphology of the evolving deposit reveals that besides electrochemistry, mechanics and crystalline defects play a major role in the growth mechanism. Based on the findings, a growth mechanism is proposed that involves the diffusion of lithium atoms from the lithium surface into grain boundaries and the insertion into crystalline defects in the metal. Crystalline defects are a result of plastic deformation and hence mechanical stimulation augments the insertion of lithium

An analysis on ensemble learning optimized medical image classification with deep convolutional neural networks

Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or multiple predictions and, thus, boost prediction performance. However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines. In this work, we proposed a reproducible medical image classification pipeline for analyzing the performance impact of the following ensemble learning techniques: Augmenting, Stacking, and Bagging. The pipeline consists of state-of-the-art preprocessing and image augmentation methods as well as 9 deep convolution neural network architectures. It was applied on four popular medical imaging datasets with varying complexity. Furthermore, 12 pooling functions for combining multiple predictions were analyzed, ranging from simple statistical functions like unweighted averaging up to more complex learning-based functions like support vector machines. Our results revealed that Stacking achieved the largest performance gain of up to 13% F1-score increase. Augmenting showed consistent improvement capabilities by up to 4% and is also applicable to single model based pipelines. Cross-validation based Bagging demonstrated significant performance gain close to Stacking, which resulted in an F1-score increase up to +11%. Furthermore, we demonstrated that simple statistical pooling functions are equal or often even better than more complex pooling functions. We concluded that the integration of ensemble learning techniques is a powerful method for any medical image classification pipeline to improve robustness and boost performance.Comment: Code: https://github.com/frankkramer-lab/ensmic ; Supplementary Material: https://doi.org/10.5281/zenodo.645791

Towards a guideline for evaluation metrics in medical image segmentation

In the last decade, research on artificial intelligence has seen rapid growth with deep learning models, especially in the field of medical image segmentation. Various studies demonstrated that these models have powerful prediction capabilities and achieved similar results as clinicians. However, recent studies revealed that the evaluation in image segmentation studies lacks reliable model performance assessment and showed statistical bias by incorrect metric implementation or usage. Thus, this work provides an overview and interpretation guide on the following metrics for medical image segmentation evaluation in binary as well as multi-class problems: Dice similarity coefficient, Jaccard, Sensitivity, Specificity, Rand index, ROC curves, Cohen’s Kappa, and Hausdorff distance. Furthermore, common issues like class imbalance and statistical as well as interpretation biases in evaluation are discussed. As a summary, we propose a guideline for standardized medical image segmentation evaluation to improve evaluation quality, reproducibility, and comparability in the research field

Robust chest CT image segmentation of COVID-19 lung infection based on limited data

Background The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. For quantitative assessment and disease monitoring medical imaging like computed tomography offers great potential as alternative to RT-PCR methods. For this reason, automated image segmentation is highly desired as clinical decision support. However, publicly available COVID-19 imaging data is limited which leads to overfitting of traditional approaches. Methods To address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods and exploiting extensive data augmentation. For further reduction of the overfitting risk, we implemented a standard 3D U-Net architecture instead of new or computational complex neural network architectures. Results Through a k-fold cross-validation on 20 CT scans as training and validation of COVID-19, we were able to develop a highly accurate as well as robust segmentation model for lungs and COVID-19 infected regions without overfitting on limited data. We performed an in-detail analysis and discussion on the robustness of our pipeline through a sensitivity analysis based on the cross-validation and impact on model generalizability of applied preprocessing techniques. Our method achieved Dice similarity coefficients for COVID-19 infection between predicted and annotated segmentation from radiologists of 0.804 on validation and 0.661 on a separate testing set consisting of 100 patients. Conclusions We demonstrated that the proposed method outperforms related approaches, advances the state-of-the-art for COVID-19 segmentation and improves robust medical image analysis based on limited data