Due to very complex structure of nasal area that is covered by facial bones, a tracking of surgical instruments
on the preoperative CT image is very important for obtaining an improved image guidance as well as preventing
surgical accidents in the paranasal sinus surgery. In this contribution, we present our recently developed an efficient
and compact navigation system for paranasal sinus surgery and its first clinical trial.
In our system, we use an optical-based 3D range imaging device intra-operatively, in order to achieve
registration and a tracking of instruments. Before the intervention, the range image of patient's face is acquired by a
3D range scanner and registered to corresponding surface extracted from the preoperative CT images. The surgical
instrument fitted with spherical markers that also can be measured by range scanning device, is tracked during the
procedure. The main advantages of our system are (a) markerless on the patient's body, (b) an easy semiautomatic
registration, (c) frameless during surgery, thus, it is feasible to update a registration and to restart the tracking when
a patient moves. In this paper, we describe a summary of used techniques in our approach including the benefits and
limitations of the system, experimental results using a precise model based on a human paranasal structure and a
first clinical trial in the surgical room.
This paper presents a segmentation method of brain tissues from MR images, invented for our image-guided neurosurgery system under development. Our goal is to segment brain tissues for creating biomechanical model. The proposed segmentation method is based on 3-D region growing and outperforms conventional approaches by stepwise usage of intensity similarities between voxels in conjunction with edge information. Since the intensity and the edge information are complementary to each other in the region-based segmentation, we use them twice by performing a coarse-to-fine extraction. First, the edge information in an appropriate neighborhood of the voxel being considered is examined to constrain the region growing. The expanded region of the first extraction result is then used as the domain for the next processing. The intensity and the edge information of the current voxel only are utilized in the final extraction. Before segmentation, the intensity parameters of the brain tissues as well as partial volume effect are estimated by using expectation-maximization (EM) algorithm in order to provide an accurate data interpretation into the extraction. We tested the proposed method on T1-weighted MR images of brain and evaluated the segmentation effectiveness comparing the results with ground truths. Also, the generated meshes from the segmented brain volume by using mesh generating software are shown in this paper.
Neurosurgical navigation systems using preoperative images have a problem in their accuracy caused by brain deformation during surgery. To address this problem the use of laser range scanner in order to obtain intraoperative cortical surface, is under study in our currently developing neurosurgical navigation system. This paper presents preliminary results of registration of intraoperatively acquired range and color images to preoperative MR images, within the context of image-guided surgery. We register images by performing two procedures: mapping of color image on the range image; and registration between color-mapped range images and preoperative medical images. The color image is mapped on the range image using camera calibration. Point-based rigid registration of preoperative images to the intraoperative images is performed through detection and matching of common fiducials in the images. Experimental results using intraoperatively acquired range images of cortical surface demonstrated the ability to perform registrations for MR images of the brain. In the future, we will focus on incorporating the above registration results into a biomechanical model of the brain to predict brain deformation during surgical procedures.
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