Anatomical Prior and Inter-Slice Consistency for Semi-Supervised Vertebral Structure Detection in 3D Ultrasound Volume.

Three-dimensional (3D) ultrasound imaging technique has been applied for scoliosis assessment, but the current assessment method only uses coronal projection images and cannot illustrate the 3D deformity and vertebra rotation. The vertebra detection is essential to reveal 3D spine information, but the detection task is challenging due to complex data and limited annotations. We propose VertMatch to detect vertebral structures in 3D ultrasound volume containing a detector and classifier. The detector network finds the potential positions of structures on transverse slice globally, and then the local patches are cropped based on detected positions. The classifier is used to distinguish whether the patches contain real vertebral structures and screen the predicted positions from the detector. VertMatch utilizes unlabeled data in a semi-supervised manner, and we develop two novel techniques for semi-supervised learning: 1) anatomical prior is used to acquire high-quality pseudo labels; 2) inter-slice consistency is used to utilize more unlabeled data by inputting multiple adjacent slices. Experimental results demonstrate that VertMatch can detect vertebra accurately in ultrasound volume and outperforms state-of-the-art methods. Moreover, VertMatch is also validated in automatic spinous process angle measurement on forty subjects with scoliosis, and the results illustrate that it can be a promising approach for the 3D assessment of scoliosis.

Development of Automatic Assessment Framework for Spine Deformity Using Freehand 3-D Ultrasound Imaging System.

A 3-D ultrasound (US) imaging technique has been studied to facilitate the diagnosis of spinal deformity without radiation. The objective of this article is to propose an assessment framework to automatically estimate spinal deformity in US spine images. The proposed framework comprises four major components, a US spine image generator, a novel transformer-based lightweight spine detector network, an angle evaluator, and a 3-D modeler. The principal component analysis (PCA) and discriminative scale space tracking (DSST) method are first adopted to generate the US spine images. The proposed detector is equipped with a redundancy queries removal (RQR) module and a regularization item to realize accurate and unique detection of spine images. Two clinical datasets, a total of 273 images from adolescents with idiopathic scoliosis, are used for the investigation of the proposed framework. The curvature is estimated by the angle evaluator, and the 3-D mesh model is established by the parametric modeling technique. The accuracy rate (AR) of the proposed detector can be achieved at 99.5%, with a minimal redundancy rate (RR) of 1.5%. The correlations between automatic curve measurements on US spine images from two datasets and manual measurements on radiographs are 0.91 and 0.88, respectively. The mean absolute difference (MAD) and standard deviation (SD) are 2.72° ± 2.14° and 2.91° ± 2.36° , respectively. The results demonstrate the effectiveness of the proposed framework to advance the application of the 3-D US imaging technique in clinical practice for scoliosis mass screening and monitoring.

A Scalable Distributed Control Algorithm for Bearing-Only Passive UAV Formation Maintenance.

Unmanned Aerial Vehicles (UAVs) can cooperate through formations to perform tasks. Wireless communication allows UAVs to exchange information, but for the situations requiring high security, electromagnetic silence is needed to avoid potential threats. The passive UAV formation maintenance strategies can fulfill the requirement of electromagnetic silence at the cost of heavy real-time computing and precise locations of UAVs. To pursue high real-time performance without the localization of UAVs, this paper proposes a scalable distributed control algorithm for bearing-only passive UAV formation maintenance. By minimizing necessary communication, pure angle information is applied to maintain UAV formations through distributed control, without the knowledge of the UAVs' precise locations. The convergency of the proposed algorithm is proven strictly and the converging radius is derived. Through simulation, the proposed algorithm is proven to be suitable for a general case and demonstrates fast convergence speed, strong anti-interference capability, and high scalability.

Janus Microdroplets with Tunable Self-Recoverable and Switchable Reflective Structural Colors.

Microdroplets made from chiral liquid crystals (CLCs) can display reflective structural colors. However, the small area of reflection and their isotropic shape limit their performance. Here, Janus microdroplets are synthesized through phase separation between CLCs and silicone oil. The as-synthesized Janus microdroplets show primary structural colors with ≈14 times larger area compared to their spherical counterparts at a specific orientation; the orientation and thus the colored/transparent states can be switched by applying a magnetic field. The color of the Janus microdroplets can be tuned ranging from red to violet by varying the concentration of the chiral dopant in the CLC phase. Due to the density difference between the two phases, the Janus microdroplets prefer to orientate the silicone oil side up vertically, enabling the self-recoverable structural color after distortion. The Janus microdroplets can be dispersed in aqueous media to track the configuration and speed of magnetic objects. They can also be patterned as multiplexed labels for data encryption. The magnetic field-responsive Janus CLC microdroplets presented here offer new insights to generate and switch reflective colors with high color saturation. It also paves the way for broader applications of CLCs, including anti-counterfeiting, data encryption, display, and untethered speed sensors.

Repeatable and Reprogrammable Shape Morphing from Photoresponsive Gold Nanorod/Liquid Crystal Elastomers.

Liquid crystal elastomers (LCEs) are of interest for applications such as soft robotics and shape-morphing devices. Among the different actuation mechanisms, light offers advantages such as spatial and local control of actuation via the photothermal effect. However, the unwanted aggregation of the light-absorbing nanoparticles in the LCE matrix will limit the photothermal response speed, actuation performance, and repeatability. Herein, a near-infrared-responsive LCE composite consisting of up to 0.20 wt% poly(ethylene glycol)-modified gold nanorods (AuNRs) without apparent aggregation is demonstrated. The high Young's modulus, 20.3 MPa, and excellent photothermal performance render repeated and fast actuation of the films (actuation within 5 s and recovery in 2 s) when exposed to 800 nm light at an average output power of ≈1.0 W cm-2 , while maintaining a large actuation strain (56%). Further, it is shown that the same sheet of AuNR/LCE film (100 µm thick) can be morphed into different shapes simply by varying the motifs of the photomasks.

Mimicry of a biophysical pathway leads to diverse pollen-like surface patterns.

A ubiquitous structural feature in biological systems is texture in extracellular matrix that gains functions when hardened, for example, cell walls, insect scales, and diatom tests. Here, we develop patterned liquid crystal elastomer (LCE) particles by recapitulating the biophysical patterning mechanism that forms pollen grain surfaces. In pollen grains, a phase separation of extracellular material into a pattern of condensed and fluid-like phases induces undulations in the underlying elastic cell membrane to form patterns on the cell surface. In this work, LCE particles with variable surface patterns were created through a phase separation of liquid crystal oligomers (LCOs) droplet coupled to homeotropic anchoring at the droplet interface, analogously to the pollen grain wall formation. Specifically, nematically ordered polydisperse LCOs and isotropic organic solvent (dichloromethane) phase-separate at the surface of oil-in-water droplets, while, different LCO chain lengths segregate to different surface curvatures simultaneously. This phase separation, which creates a distortion in the director field, is in competition with homeotropic anchoring induced by sodium dodecyl sulfate (SDS). By tuning the polymer chemistry of the system, we are able to influence this separation process and tune the types of surface patterns in these pollen-like microparticles. Our study reveals that the energetically favorable biological mechanism can be leveraged to offer simple yet versatile approaches to synthesize microparticles for mechanosensing, tissue engineering, drug delivery, energy storage, and displays.

Intrinsically reversible superglues via shape adaptation inspired by snail epiphragm.

Adhesives are ubiquitous in daily life and industrial applications. They usually fall into one of two classes: strong but irreversible (e.g., superglues) or reversible/reusable but weak (e.g., pressure-sensitive adhesives and biological and biomimetic surfaces). Achieving both superstrong adhesion and reversibility has been challenging. This task is particularly difficult for hydrogels that, because their major constituent is liquid water, typically do not adhere strongly to any material. Here, we report a snail epiphragm-inspired adhesion mechanism where a polymer gel system demonstrates superglue-like adhesion strength (up to 892 N⋅cm-2) that is also reversible. It is applicable to both flat and rough target surfaces. In its hydrated state, the softened gel conformally adapts to the target surface by low-energy deformation, which is locked upon drying as the elastic modulus is raised from hundreds of kilopascals to ∼2.3 GPa, analogous to the action of the epiphragm of snails. We show that in this system adhesion strength is based on the material's intrinsic, especially near-surface, properties and not on any near-surface structure, providing reversibility and ease of scaling up for practical applications.