E74 Is Involved in the Regulation of Chitin Biosynthesis and Development of the Cigarette Beetle, Lasioderma serricorne.

The growth and development of insects are mainly governed by juvenile hormone (JH) and molting hormone, which play essential roles in metamorphosis, reproductive control, and signal transduction. However, it remains uncertain whether E74, a crucial transcription factor within the 20- hydroxyecdysone (20E) signaling pathway, is involved in regulating chitin biosynthesis in Lasioderma serricorne. In this study, the E74 gene was identified in L.

An Arthroscopic Robotic System for Meniscoplasty with Autonomous Operation Ability.

Meniscoplasty is a common surgical procedure used to treat meniscus tears. During the operation, there are often key challenges such as a limited visual field, a narrow operating space, and difficulties in controlling the resection range. Therefore, this study developed an arthroscopic robotic system with the ability of autonomous meniscus resection to achieve better surgical outcomes.

Liver lesion segmentation in ultrasound: A benchmark and a baseline network.

Accurate liver lesion segmentation in ultrasound is a challenging task due to high speckle noise, ambiguous lesion boundaries, and inhomogeneous intensity distribution inside the lesion regions. This work first collected and annotated a dataset for liver lesion segmentation in ultrasound. In this paper, we propose a novel convolutional neural network to learn dual self-attentive transformer features for boosting liver lesion segmentation by leveraging the complementary information among non-local features encoded at different layers of the transformer architecture.

Efficient Breast Lesion Segmentation From Ultrasound Videos Across Multiple Source-Limited Platforms.

Medical video segmentation is fundamentally important in clinical diagnosis and treatment procedures, offering dynamic tracking of breast lesions across frames in ultrasound videos for improved segmentation performance. However, existing approaches face challenges in striking a balance between segmentation performance and inference speed, hindering real-time application in resource-constrained medical environments. In order to address these limitations, we present BaS, a blazing-fast on-device breast lesion segmentation model.

Advances of surgical robotics: image-guided classification and application.

Surgical robotics application in the field of minimally invasive surgery has developed rapidly and has been attracting increasingly more research attention in recent years. A common consensus has been reached that surgical procedures are to become less traumatic and with the implementation of more intelligence and higher autonomy, which is a serious challenge faced by the environmental sensing capabilities of robotic systems. One of the main sources of environmental information for robots are images, which are the basis of robot vision.

Ultrasound Report Generation With Cross-Modality Feature Alignment via Unsupervised Guidance.

Automatic report generation has arisen as a significant research area in computer-aided diagnosis, aiming to alleviate the burden on clinicians by generating reports automatically based on medical images. In this work, we propose a novel framework for automatic ultrasound report generation, leveraging a combination of unsupervised and supervised learning methods to aid the report generation process. Our framework incorporates unsupervised learning methods to extract potential knowledge from ultrasound text reports, serving as the prior information to guide the model in aligning visual and textual features, thereby addressing the challenge of feature discrepancy.

A Soft-Reference Breast Ultrasound Image Quality Assessment Method That Considers the Local Lesion Area.

The quality of breast ultrasound images has a significant impact on the accuracy of disease diagnosis. Existing image quality assessment (IQA) methods usually use pixel-level feature statistical methods or end-to-end deep learning methods, which focus on the global image quality but ignore the image quality of the lesion region. However, in clinical practice, doctors' evaluation of ultrasound image quality relies more on the local area of the lesion, which determines the diagnostic value of ultrasound images.

CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data.

Accurate segmentation of kidney in ultrasound images is a vital procedure in clinical diagnosis and interventional operation. In recent years, deep learning technology has demonstrated promising prospects in medical image analysis. However, due to the inherent problems of ultrasound images, data with annotations are scarce and arduous to acquire, hampering the application of data-hungry deep learning methods.

Automatic registration and precise tumour localization method for robot-assisted puncture procedure under inconsistent breath-holding conditions.

Background: During percutaneous puncture procedure, breath holding is subjectively controlled by patients, and it is difficult to ensure consistent tumour position between the preoperative CT scanning phase and the intraoperative puncture phase. In addition, the manual registration process is time-consuming and has low accuracy.

Methods: We have proposed an automatic registration method using optical markers and a tumour breath-holding position estimation model based on the support vector regression algorithm.

Diffeomorphic respiratory motion estimation of thoracoabdominal organs for image-guided interventions.

Purpose: Percutaneous image-guided interventions are commonly used for the diagnosis and treatment of cancer. In practice, physiological breathing-induced motion increases the difficulty of accurately inserting needles into tumors without impairing the surrounding vital structures. In this work, we propose a data-driven patient-specific hierarchical respiratory motion estimation framework to accurately estimate the position of a tumor and surrounding vital tissues in real time.

Design and control of a MRI-compatible pneumatic needle puncture robot.

Percutaneous needle puncture operation is widely used in the image-guided interventions, including biopsy and ablation. MRI guidance has the advantages of high-resolution soft tissue imaging and thermal monitoring during energy-based ablation. This paper proposes the design of a 5-DOF pneumatic needle puncture robot, with all the cylinders, sensors and structure material MRI-compatible.

A sensorless force-feedback system for robot-assisted laparoscopic surgery.

The existing surgical robots for laparoscopic surgery offer no or limited force feedback, and there are many problems for the traditional sensor-based solutions. This paper builds a teleoperation surgical system and validates the effectiveness of sensorless force feedback. The tool-tissue interaction force at the surgical grasper tip is estimated using the driving motor's current, and fed back to the master robot with position-force bilateral control algorithm.

Overlapping radiofrequency ablation planning and robot-assisted needle insertion for large liver tumors.

Background: We aimed at facilitating percutaneous radiofrequency ablation (RFA) for large tumors with accurate overlapping ablation planning and robot-assisted needle insertion from a single incision port (SIP).

Methods: We developed a personalized and quantitative RFA planning method to obtain multiple needle overlapping ablation planning through a single incision. A robot with a remote center of motion mechanism was designed to perform needle insertions through a SIP according to the planning.

Estimating Tool-Tissue Forces Using a 3-Degree-of-Freedom Robotic Surgical Tool.

Robot-assisted minimally invasive surgery (MIS) has gained popularity due to its high dexterity and reduced invasiveness to the patient; however, due to the loss of direct touch of the surgical site, surgeons may be prone to exert larger forces and cause tissue damage. To quantify tool-tissue interaction forces, researchers have tried to attach different kinds of sensors on the surgical tools. This sensor attachment generally makes the tools bulky and/or unduly expensive and may hinder the normal function of the tools; it is also unlikely that these sensors can survive harsh sterilization processes.

Sensorless Force Sensing for Minimally Invasive Surgery.

Robotic minimally invasive surgery (R-MIS) has achieved success in various procedures; however, the lack of haptic feedback is considered by some to be a limiting factor. The typical method to acquire tool-tissue reaction forces is attaching force sensors on surgical tools, but this complicates sterilization and makes the tool bulky. This paper explores the feasibility of using motor current to estimate tool-tissue forces and demonstrates acceptable results in terms of time delay and accuracy.