Regrafting submillimeter-scale ferromagnetic soft continuums.

Submillimeter-scale ferromagnetic soft continuums (FSCs) own innate skills in performing desirable and delicate bending for confined space navigation, especially in biological lumens. However, such tiny structures are difficult to endow with complex designs, thereby challenging to realize more sophisticated functions for various purposes, especially in vivo therapies and manipulations. Inspired by grafting for muscles and plants, we propose submillimeter-scale FSCs that can actively divide into pieces at any region, and conversely, the pieces can actively graft to each other to form the original structure or novel shapes.

Rethinking data imbalance in class incremental surgical instrument segmentation.

In surgical instrument segmentation, the increasing variety of instruments over time poses a significant challenge for existing neural networks, as they are unable to effectively learn such incremental tasks and suffer from catastrophic forgetting. When learning new data, the model experiences a sharp performance drop on previously learned data. Although several continual learning methods have been proposed for incremental understanding tasks in surgical scenarios, the issue of data imbalance often leads to a strong bias in the segmentation head, resulting in poor performance.

PedSemiSeg: Pedagogy-inspired semi-supervised polyp segmentation.

Recent advancements in deep learning techniques have contributed to developing improved polyp segmentation methods, thereby aiding in the diagnosis of colorectal cancer and facilitating automated surgery like endoscopic submucosal dissection (ESD). However, the scarcity of well-annotated data poses challenges by increasing the annotation burden and diminishing the performance of fully-supervised learning approaches. Additionally, distribution shifts due to variations among patients and medical centers require the model to generalize well during testing.

PDZSeg: adapting the foundation model for dissection zone segmentation with visual prompts in robot-assisted endoscopic submucosal dissection.

Purpose: The intricate nature of endoscopic surgical environments poses significant challenges for the task of dissection zone segmentation. Specifically, the boundaries between different tissue types lack clarity, which can result in significant segmentation errors, as the models may misidentify or overlook object edges altogether. Thus, the goal of this work is to achieve the precise dissection zone suggestion under these challenges during endoscopic submucosal dissection (ESD) procedures and enhance the overall safety of ESD.

Fine-Grained Classification Reveals Angiopathological Heterogeneity of Port Wine Stains Using OCT and OCTA Features.

Accurate classification of port wine stains (PWS, vascular malformations present at birth), is critical for subsequent treatment planning. However, the current method of classifying PWS based on the external skin appearance rarely reflects the underlying angiopathological heterogeneity of PWS lesions, resulting in inconsistent outcomes with the common vascular-targeted photodynamic therapy (V-PDT) treatments. Conversely, optical coherence tomography angiography (OCTA) is an ideal tool for visualizing the vascular malformations of PWS.

Untethered bistable origami crawler for confined applications.

Magnetically actuated miniature origami crawlers are capable of robust locomotion in confined environments but are limited to passive functionalities. Here, we propose a bistable origami crawler that can shape-morph to access two separate regimes of folding degrees of freedom that are separated by an energy barrier. Using the modified bistable V-fold origami crease pattern as the fundamental unit of the crawler, we incorporated internal permanent magnets to enable untethered shape-morphing.

Large-Scale Selective Micropatterning with Robotics nDEP Tweezers and Hydrogel Encapsulation.

Creating diverse microparticle patterns on a large scale enhances the performance and efficiency of biochemical analytics. Current techniques exhibit limitations in achieving diverse microparticle patterns on a large scale, primarily focusing on patterning particles of the same type with limited flexibility and accessibility. Moreover, accessibility to patterned particles without a fixed formation poses additional challenges.

Motor-free telerobotic endomicroscopy for steerable and programmable imaging in complex curved and localized areas.

Intraluminal epithelial abnormalities, potential precursors to significant conditions like cancer, necessitate early detection for improved prognosis. We present a motor-free telerobotic optical coherence tomography (OCT) endoscope that offers high-resolution intraluminal imaging and overcomes the limitations of traditional systems in navigating curved lumens. This system incorporates a compact magnetic rotor with a rotatable diametrically magnetized cylinder permanent magnet (RDPM) and a reflector, effectively mitigating thermal and electrical risks by utilizing an external magnetic field to maintain temperature increases below 0.

PneumaOCT: Pneumatic optical coherence tomography endoscopy for targeted distortion-free imaging in tortuous and narrow internal lumens.

The complex anatomy of internal luminal organs, like bronchioles, poses challenges for endoscopic optical coherence tomography (OCT). These challenges include limited steerability for targeted imaging and nonuniform rotation distortion (NURD) with proximal scanning. Using rotary micromotors for distal scanning could address NURD but raises concerns about electrical safety and costs.

How Do Combustions Actuate High-Speed Soft Robots?

The combustion actuation method opens a unique pathway for high-performance soft robots, allowing for high accelerations in multifunctional applications. Along with multifunctionality come great challenges in effective robot structure design, accurate control and prediction of combustion-actuated motions, and practical implementation of various applications. However, research in this nascent field remains fragmented, lacking central guiding principles.

Fully Organic Sensors for Continuous Real-Time Digestion Monitoring.

Monitoring the gastric digestive function is important for the diagnosis of gastric disorders and drug development. However, there is no report on the in situ and real-time monitoring of digestive functions. Herein, we report a flexible fully organic sensor to effectively monitor protein digestion in situ in a simulated gastric environment for the first time.

SimCol3D - 3D reconstruction during colonoscopy challenge.

Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform.

FARN: Fetal Anatomy Reasoning Network for Detection With Global Context Semantic and Local Topology Relationship.

Accurate recognition of fetal anatomical structure is a pivotal task in ultrasound (US) image analysis. Sonographers naturally apply anatomical knowledge and clinical expertise to recognizing key anatomical structures in complex US images. However, mainstream object detection approaches usually treat each structure recognition separately, overlooking anatomical correlations between different structures in fetal US planes.

Surgical-DINO: adapter learning of foundation models for depth estimation in endoscopic surgery.

Purpose: Depth estimation in robotic surgery is vital in 3D reconstruction, surgical navigation and augmented reality visualization. Although the foundation model exhibits outstanding performance in many vision tasks, including depth estimation (e.g.

Privacy-Preserving Synthetic Continual Semantic Segmentation for Robotic Surgery.

Deep Neural Networks (DNNs) based semantic segmentation of the robotic instruments and tissues can enhance the precision of surgical activities in robot-assisted surgery. However, in biological learning, DNNs cannot learn incremental tasks over time and exhibit catastrophic forgetting, which refers to the sharp decline in performance on previously learned tasks after learning a new one. Specifically, when data scarcity is the issue, the model shows a rapid drop in performance on previously learned instruments after learning new data with new instruments.

Rethinking pain communication of patients with Alzheimer's disease through E-textile interaction design.

Older individuals are easily prone to chronic pain. Due to the complexity of chronic pain, most elderly often have difficulty expressing pain to others to seek assistance, especially those with Alzheimer's disease (AD). The caregivers cannot instantly discover the patients' pain condition and provide timely pain management.

Data augmentation using continuous conditional generative adversarial networks for regression and its application to improved spectral sensing.

Machine learning-assisted spectroscopy analysis faces a prominent constraint in the form of insufficient spectral samples, which hinders its effectiveness. Meanwhile, there is a lack of effective algorithms to simulate synthetic spectra from limited samples of real spectra for regression models in continuous scenarios. In this study, we introduced a continuous conditional generative adversarial network (CcGAN) to autonomously generate synthetic spectra.

TransFSM: Fetal Anatomy Segmentation and Biometric Measurement in Ultrasound Images Using a Hybrid Transformer.

Biometric parameter measurements are powerful tools for evaluating a fetus's gestational age, growth pattern, and abnormalities in a 2D ultrasound. However, it is still challenging to measure fetal biometric parameters automatically due to the indiscriminate confusing factors, limited foreground-background contrast, variety of fetal anatomy shapes at different gestational ages, and blurry anatomical boundaries in ultrasound images. The performance of a standard CNN architecture is limited for these tasks due to the restricted receptive field.

Novel miniature transendoscopic telerobotic system for endoscopic submucosal dissection (with videos).

Background And Aims: The lack of tissue traction and instrument dexterity to allow for adequate visualization and effective dissection were the main issues in performing endoscopic submucosal dissection (ESD). Robot-assisted systems may provide advantages. In this study we developed a novel transendoscopic telerobotic system and evaluated its performance in ESD.

Rethinking exemplars for continual semantic segmentation in endoscopy scenes: Entropy-based mini-batch pseudo-replay.

Endoscopy is a widely used technique for the early detection of diseases or robotic-assisted minimally invasive surgery (RMIS). Numerous deep learning (DL)-based research works have been developed for automated diagnosis or processing of endoscopic view. However, existing DL models may suffer from catastrophic forgetting.

Three-Dimensional Collision Avoidance Method for Robot-Assisted Minimally Invasive Surgery.

In the robot-assisted minimally invasive surgery, if a collision occurs, the robot system program could be damaged, and normal tissues could be injured. To avoid collisions during surgery, a 3-dimensional collision avoidance method is proposed in this paper. The proposed method is predicated on the design of 3 strategic vectors: the collision-with-instrument-avoidance (CI) vector, the collision-with-tissues-avoidance (CT) vector, and the constrained-control (CC) vector.

Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images.

Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in airway segmentation, particularly for the small branches of the airway. These difficulties arise due to the constraints of limited labeling and failure to meet clinical use requirements in COPD.

Lightweight Pneumatically Elastic Backbone Structure with Modular Construction and Nonlinear Interaction for Soft Actuators.

There has been a growing need for soft robots operating various force-sensitive tasks due to their environmental adaptability, satisfactory controllability, and nonlinear mobility unique from rigid robots. It is of desire to further study and that are the main influence factors to the actuations of lightweight soft actuators. In this study, we present a design principle on lightweight pneumatically elastic backbone structure (PEBS) with the modular construction for soft actuators, which contains a backbone printed as one piece and a common strip balloon.