The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor segmentation. Glioma, a type of brain tumor, can appear at different locations with different shapes and sizes. Manual segmentation of brain tumor regions is not only time-consuming but also prone to human error, and its performance depends on pathologists’ experience. In this paper, we tackle this problem by applying a fully convolutional neural network SegNet to 3D data sets for four MRI modalities (Flair, T1, T1ce, and T2) for automated segmentation of brain tumor and subtumor parts, including necrosis, edema, and enhancing tumor. To further improve tumor segmentation, the four separately trained SegNet models are integrated by post-processing to produce four maximum feature maps by fusing the machine-learned feature maps from the fully convolutional layers of each trained model. The maximum feature maps and the pixel intensity values of the original MRI modalities are combined to encode interesting information into a feature representation. Taking the combined feature as input, a decision tree (DT) is used to classify the MRI voxels into different tumor parts and healthy brain tissue. Evaluating the proposed algorithm on the dataset provided by the Brain Tumor Segmentation 2017 (BraTS 2017) challenge, we achieved F-measure scores of 0.85, 0.81, and 0.79 for whole tumor, tumor core, and enhancing tumor, respectively. Experimental results demonstrate that using SegNet models with 3D MRI datasets and integrating the four maximum feature maps with pixel intensity values of the original MRI modalities has potential to perform well on brain tumor segmentation.

Acoustofluidics offers contact-free manipulation of particles and fluids, enabling their uses in various life sciences, such as for biological and medical applications. Recently, there have been extensive studies on acoustic streaming-based acoustofluidics, which are formed inside a liquid agitated by leaky surface acoustic waves (SAWs) through applying radio frequency signals to interdigital transducers (IDTs) on a piezoelectric substrate. This paper aims to describe acoustic streaming-based acoustofluidics and provide readers with an unbiased perspective to determine which IDT structural designs and techniques are most suitable for their research. This review, first, qualitatively and quantitatively introduces underlying physics of acoustic streaming. Then, it comprehensively discusses the fundamental designs of IDT technology for generating various types of acoustic streaming phenomena. Acoustic streaming-related methodologies and the corresponding biomedical applications are highlighted and discussed, according to either standing surface acoustic waves or traveling surface acoustic waves generated, and also sessile droplets or continuous fluids used. Traveling SAW-based acoustofluidics generate various physical phenomena including mixing, concentration, rotation, pumping, jetting, nebulization/atomization, and droplet generation, as well as mixing and concentration of liquid in a channel/chamber. Standing SAWs induce streaming for digital and continuous acoustofluidics, which can be used for mixing, sorting, and trapping in a channel/chamber. Key challenges, future developments, and directions for acoustic streaming-based acoustofluidics are finally discussed.

Polymer materials exhibit unique properties in the fabrication of optical waveguide devices, electromagnetic devices, and bio-devices. Direct laser writing (DLW) technology is widely used for micro-structure fabrication due to its high processing precision, low cost, and no need for mask exposure. This paper reviews the latest research progresses of polymer-based micro/nano-devices fabricated using the DLW technique as well as their applications. In order to realize various device structures and functions, different manufacture parameters of DLW systems are adopted, which are also investigated in this work. The flexible use of the DLW process in various polymer-based microstructures, including optical, electronic, magnetic, and biomedical devices are reviewed together with their applications. In addition, polymer materials which are developed with unique properties for the use of DLW technology are also discussed.

A flexible thin film acoustofluidic device generates hybrid wave modes for microfluidic actuation (Lamb waves) and biosensing (thickness shear waves).

Extracellular vesicles (EVs) are secreted nanostructures that play various roles in critical cancer processes. They operate as an intercellular communication system, transferring complex sets of biomolecules from cell to cell. The concentration of EVs is difficult to decipher, and there is an unmet technological need for improved (faster, simpler, and gentler) approaches to isolate EVs from complex matrices. Herein, an acoustofluidic concentration of extracellular vesicles (ACEV) is presented, based on a thin‐film printed circuit board with interdigital electrodes mounted on a piezoelectric substrate. An angle of 120° is identified between the electrodes and the reference flat of the piezoelectric substrate for simultaneous generation of Rayleigh and shear horizontal waves. The dual waves create a complex acoustic field in a droplet, resulting in effective concentration of nanoparticles and EVs. The ACEV is able to concentrate 20 nm nanospheres within 105 s and four EV dilutions derived from the human prostate cancer (Du145) cell line in approximately 30 s. Cryo‐electron microscopy confirmed the preservation of EV integrity. The ACEV device holds great potential to revolutionize investigations of EVs. Its faster, simpler, and gentler approach to EV isolation and concentration can save time and effort in phenotypic and functional studies of EVs.