The estimation of the pose of surgical instruments is important in Robot-assisted Minimally Invasive Surgery (RMIS) to assist surgical navigation and enable autonomous robotic task execution. The performance of current instrument pose estimation methods deteriorates significantly in the presence of partial tool visibility, occlusions, and changes in the surgical scene. In this work, a vision-based framework is proposed for markerless estimation of the 6DoF pose of surgical instruments. To deal with partial instrument visibility, a keypoint object representation is used and stable and accurate instrument poses are computed using a PnP solver. To boost the learning process of the model under occlusion, a new mask-based data augmentation approach has been proposed. To validate the model, a dataset for instrument pose estimation with highly accurate ground truth data has been generated using different surgical robotic instruments. The proposed network can achieve submillimeter accuracy and the experimental results verify its generalisability to different shapes of occlusion.
Despite the benefits of minimally invasive surgery, interventions such as laparoscopic liver surgery present unique challenges, like the significant anatomical differences between preoperative images and intraoperative scenes due to pneumoperitoneum, patient pose, and organ manipulation by surgical instruments. To address these challenges, a method for intraoperative three-dimensional reconstruction of the surgical scene, including vessels and tumors, without altering the surgical workflow, is proposed. The technique combines neural radiance field reconstructions from tracked laparoscopic videos with ultrasound three-dimensional compounding.
The estimation of the pose of surgical instruments is important in Robot-assisted Minimally Invasive Surgery (RMIS) to assist surgical navigation and enable autonomous robotic task execution. The performance of current instrument pose estimation methods deteriorates significantly in the presence of partial tool visibility, occlusions, and changes in the surgical scene. In this work, a vision-based framework is proposed for markerless estimation of the 6DoF pose of surgical instruments.
Background: Diabetic retinopathy (DR) is the most important complication of Type 2 Diabetes (T2D) in eyes. Despite its prevalence, the early detection and management of DR continue to pose considerable challenges. Our research aims to elucidate potent drug targets that could facilitate the identification of DR and propel advancements in its therapeutic strategies.
Per- and polyfluoroalkyl substances (PFAS) have become a major focus of research due to their widespread environmental presence and adverse health effects associated with human exposure. PFAS include legacy and emerging structures and are characterized by a range of functional groups and carbon-fluorine chains that vary in length (from fewer than 3 carbons to more than 7 carbons). Research has linked PFAS exposure to an array of health concerns, ranging from developmental and reproductive disorders to immune system impairments and an increased risk of certain cancers.
Antibiotic resistance genes (ARGs) pose serious threats to environmental and public health, and monitoring ARGs in wastewater is a growing need because wastewater is an important source. Microfluidic devices can integrate basic functional units involved in sample assays on a small chip, through the precise control and manipulation of micro/nanofluids in micro/nanoscale spaces, demonstrating the great potential of ARGs detection in wastewater. Here, we (1) summarize the state of the art in microfluidics for recognizing ARGs, (2) determine the strengths and weaknesses of portable microfluidic chips, and (3) assess the potential of portable microfluidic chips to detect ARGs in wastewater.
