Laparoscopy Uses in Urology Augmented Reality System

 

Uses in Urology

In urology, the use of AR has mainly reported for the performance of partial nephrectomies. Fewer than 20 cases have reported to date with very variable methodologies. Several registration systems have described but no reliable system for monitoring the organ. Once again, this is where the technical solutions that we propose and that we have evaluated come into play. 

For a large number of reasons use of simulators is a prudent choice like Laparoscopic Needle Driver.

For prostatectomy the use of AR from preoperative CT and / or MRI images has also been reported. The use in particular of the combination of the real-time ultrasound image and the laparoscopic image has also reported. This should not confuse with intraoperative ultrasound guidance, which has described for a longer time and for which there is no registration of the two images, which of course limits the decision aid and the gesture for the surgeon. 

Here again the techniques used require in the majority of cases the addition of visual markers, figure 21). Here again our system, which does not require visual or magnetic markers, could be of major interest.

Principle of Our Augmented Reality System

• Steps are essential for the functioning of AR in general and our system in particular:

• Acquisition of imaging data and creation of the associated 3D model 

• Creation of the3dperoperative model and initial registration

• Real-time tracking.

• AR display

Acquisition of imaging data and creation of the associated 3D model: The goal is to create a three-dimensional model of all the imaging data that the operator will want to see displayed including the organ “target ". This step requires the segmentation or “contouring” of the data that we want to integrate into the AR. We must therefore determine the outline of each element that we want to integrate into augmented reality: to integrate the 3D image of the uterus, its shape must be determined in 3D. 

The image that we have of this organ consists of a 2D section. By delimiting the contour of an organ on all the axial sections for example and by adding all these contours, we will be able to determine its 3D shape. This step can be automatic or semi-automatic. Several software and / or computer hardware. Other software is also available: Mimics. That we use and which can run on a standard PC. 

Use of Radiology

Their performance seems globally satisfactory and comparable. Tissues with high CT, MRI or ultrasound contrast. Fully automatic technical segmentation solutions have described. It is in fact simply a question of replacing the points of a given intensity by a color and a given transparency. This simple transfer feature is available on all workstations radiologists currently. 

Unfortunately, this automatic or semi-automatic segmentation is essentially limited once again to organs with strong contrast or in any case homogeneous and well differentiated from adjacent organs. Several research teams are starting to describe segmentation systems for the uterus as well as for myomas with the application of pre- and post-procedure evaluation during focused ultrasound treatment. Solutions for segmenting ultrasound images of the uterus have also proposed. These solutions have developed in particular for irradiation in cervical cancer, making it possible to define the irradiation field more precisely. All of this is also part of a computer-aided analysis of anatomy and is therefore doomed to important future developments. 

Heterogeneous Segmentation

Strongly convex, heterogeneous structures with ill-defined boundaries all difficulties for uterine segmentation. We have chosen not to focus our research on these segmentation methods because they require development on their own, other teams also working on kidney segmentation algorithms. We used a simple but robust software. A free open-source software allows semi-automatic segmentation. 

Homogeneous Segmentation

Several 2D instruments of the program exist, the "region growing" function allows you to select an entire homogeneous region then the operator will manually refine the segmentation. The operator checks on the corresponding sagittal and coronal slices that the segmentation is correct. The more the surface of the “region growing” function makes it possible to select an entire homogeneous region then the operator will manually refine the segmentation. 

Results

The operator checks on the corresponding sagittal and coronal slices that the segmentation is correct. The more the surface of the “region growing” function makes it possible to select an entire homogeneous region then the operator will manually refine the segmentation. The operator checks on the corresponding sagittal and coronal slices that the segmentation is correct. The more the surface of using software for segmentation.

For more information visit our website: www.gerati.com


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