Maternal and pregnancy factors contributing to the association between area deprivation and infant mortality in England: a retrospective cohort study.

Background: Socioeconomic inequality in infant mortality in the UK is rising. This study aims to identify contributory maternal and pregnancy factors that can explain the known association between area deprivation and infant mortality.

Methods: A cohort study was conducted using Clinical Practice Research Datalink (CPRD) primary care data between 2004 and 2019 linked to the Index of Multiple Deprivation (IMD), and infant mortality from the Office for National Statistics death data.

Mask then classify: multi-instance segmentation for surgical instruments.

Purpose: The detection and segmentation of surgical instruments has been a vital step for many applications in minimally invasive surgical robotics. Previously, the problem was tackled from a semantic segmentation perspective, yet these methods fail to provide good segmentation maps of instrument types and do not contain any information on the instance affiliation of each pixel. We propose to overcome this limitation by using a novel instance segmentation method which first masks instruments and then classifies them into their respective type.

Kidney edge detection in laparoscopic image data for computer-assisted surgery : Kidney edge detection.

Purpose: In robotic-assisted kidney surgery, computational methods make it possible to augment the surgical scene and potentially improve patient outcome. Most often, soft-tissue registration is a prerequisite for the visualization of tumors and vascular structures hidden beneath the surface. State-of-the-art volume-to-surface registration methods, however, are computationally demanding and require a sufficiently large target surface.

Adjoint Transformation Algorithm for Hand-Eye Calibration with Applications in Robotic Assisted Surgery.

Hand-eye calibration aims at determining the unknown rigid transformation between the coordinate systems of a robot arm and a camera. Existing hand-eye algorithms using closed-form solutions followed by iterative non-linear refinement provide accurate calibration results within a broad range of robotic applications. However, in the context of surgical robotics hand-eye calibration is still a challenging problem due to the required accuracy within the millimetre range, coupled with a large displacement between endoscopic cameras and the robot end-effector.

Vision-based and marker-less surgical tool detection and tracking: a review of the literature.

In recent years, tremendous progress has been made in surgical practice for example with Minimally Invasive Surgery (MIS). To overcome challenges coming from deported eye-to-hand manipulation, robotic and computer-assisted systems have been developed. Having real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy is a key ingredient for such systems.

Toward detection and localization of instruments in minimally invasive surgery.

Methods for detecting and localizing surgical instruments in laparoscopic images are an important element of advanced robotic and computer-assisted interventions. Robotic joint encoders and sensors integrated or mounted on the instrument can provide information about the tool's position, but this often has inaccuracy when transferred to the surgeon's point of view. Vision sensors are currently a promising approach for determining the position of instruments in the coordinate frame of the surgical camera.