Preconcentrating samples of dilute particles or cells to a detectable level is required in many chemical, environmental and biomedical applications. A variety of force fields have thus far been demonstrated to capture and accumulate particles and cells in microfluidic devices, which, however, all take place within the region of microchannels and may potentially cause channel clogging. This work presents a new method for the electrokinetic preconcentration of 1 μm-diameter polystyrene particles and E. coli cells in a very-low-conductivity medium inside a microfluidic reservoir. The entire microchannel can hence be saved for a post-concentration analysis. This method exploits the strong recirculating flows of induced-charge electroosmosis to concentrate particles and cells near the corners of the reservoir-microchannel interface. Positive dielectrophoresis is found to also play a role when small microchannels are used at high electric fields. Such an in-reservoir electrokinetic preconcentration method can be easily implemented in a parallel mode to increase the flow throughput, which may potentially be used to preconcentrate bacterial pathogens in water.
The separation of particles and cells in a uniform mixture has been extensively studied as a necessity in many chemical and biomedical engineering and research fields. This work demonstrates a continuous charge-based separation of fluorescent and plain spherical polystyrene particles with comparable sizes in a ψ-shaped microchannel via the wall-induced electrical lift. The effects of both the direct current electric field in the main-branch and the electric field ratio in between the inlet branches for sheath fluid and particle mixture are investigated on this electrokinetic particle separation. A Lagrangian tracking method based theoretical model is also developed to understand the particle transport in the microchannel and simulate the parametric effects on particle separation. Moreover, the demonstrated charge-based separation is applied to a mixture of yeast cells and polystyrene particles with similar sizes. Good separation efficiency and purity are achieved for both the cells and the particles.
BUS-Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field of BUS. Method: Four publicly available datasets were compiled creating an overall set of 1154 BUS images, from five different scanner types. Full dataset details have been provided, which include clinical labels and detailed annotations. Furthermore, nine state-of -the-art deep learning architectures were selected to form the initial benchmark segmentation result, tested using five-fold cross-validation and MANOVA/ANOVA with Tukey statistical significance test with a threshold of 0.01. Additional evaluation of these architectures was conducted, exploring possible training bias, and lesion size and type effects. Results: Of the nine state-of -the-art benchmarked architectures, Mask R-CNN obtained the highest overall results, with the following mean metric scores: Dice score of 0.851, intersection over union of 0.786 and pixel accuracy of 0.975. MANOVA/ANOVA and Tukey test results showed Mask R-CNN to be statistically significant better compared to all other benchmarked models with a p-value > 0.01. Moreover, Mask R-CNN achieved the highest mean Dice score of 0.839 on an additional 16 image dataset, that contained multiple lesions per image. Further analysis on regions of interest was conducted, assessing Hamming distance, depth-to-width ratio (DWR), circularity, and elongation, which showed that the Mask R-CNN's segmentations maintained the most morphological features with correlation coefficients of 0.888, 0.532, 0.876 for DWR, circularity, and elongation, respectively. Based on the correlation coefficients, statistical test indicated that Mask R-CNN was only significantly different to Sk-U-Net. Conclusions: BUS-Set is a fully reproducible benchmark for BUS lesion segmentation obtained through the use of public datasets and GitHub. Of the state-of -the-art convolution neural network (CNN)-based architectures, Mask R-CNN achieved the highest performance overall, further analysis indicated that a training bias may have occurred due to the lesion size variation in the dataset. All dataset and architecture details are available at GitHub: https://github.com/ corcor27/BUS-Set, which allows for a fully reproducible benchmark.
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