Lung nodule pre-diagnosis and insertion path planning for chest CT images.

Medical image processing has proven to be effective and feasible for assisting oncologists in diagnosing lung, thyroid, and other cancers, especially at early stage. However, there is no reliable method for the recognition, screening, classification, and detection of nodules, and even deep learning-based methods have limitations. In this study, we mainly explored the automatic pre-diagnosis of lung nodules with the aim of accurately identifying nodules in chest CT images, regardless of the benign and malignant nodules, and the insertion path planning of suspected malignant nodules, used for further diagnosis by robotic-based biopsy puncture.

Experimental study of the optimum puncture pattern of robot-assisted needle insertion into hyperelastic materials.

The robot-assisted insertion surgery plays a crucial role in biopsy and therapy. This study focuses on determining the optimum puncture pattern for robot-assisted insertion, aiming at the matching problem of needle insertion parameters, thereby to reduce the pain for patients and to improve the reachability to the lesion point. First, a 6-degrees of freedom (DOFs) Computed Tomography (CT)-guided surgical robotic system for minimally invasive percutaneous lung is developed and used to perform puncture experiments.

Lesion positioning method of a CT-guided surgical robotic system for minimally invasive percutaneous lung.

Background: Robot-assisted puncture has gradually attracted more attention and practical clinical application. The lesion positioning and the needle positioning are the basis to ensure the accuracy of puncture and the key techniques in insertion operation.

Methods: A lesion positioning method is established which is realized only by the robot-CT system without using external positioning system, and an omnidirectional needle positioning method is also developed and realized by using VRCM, in order to make the puncture needle always keep pointing to the lesion point.