Abstract
The emerging mobile fluoroscopic 3D technology linked with a navigation
system combines the advantages of CT-based and C-arm-based navigation. The
intra-operative, automatic segmentation of 3D fluoroscopy datasets enables the
combined visualization of surgical instruments and anatomical structures
for enhanced planning, surgical eye-navigation and landmark digitization.
We performed a thorough evaluation of several segmentation algorithms using
a large set of data from different anatomical regions and man-made phantom
objects. The analyzed segmentation methods include
automatic thresholding, morphological operations, an adapted region growing
method and an implicit 3D geodesic snake method. In regard to computational
efficiency, all methods performed within acceptable limits on a standard
Desktop PC (30sec-5min). In general, the best results were obtained with
datasets from long bones, followed by extremities. The segmentations of
spine, pelvis and shoulder datasets were generally of poorer quality. As
expected, the threshold-based methods produced the worst results. The
combined thresholding and morphological operations methods were considered
appropriate for a smaller set of clean images. The region growing method
performed generally much better in regard to computational efficiency and
segmentation correctness, especially for datasets of joints, and lumbar and
cervical spine regions. The less efficient implicit snake method was able
to additionally remove wrongly segmented skin tissue regions. This study
presents a step towards efficient intra-operative segmentation of 3D fluoroscopy
datasets, but there is room for improvement. Next, we plan to study model-based
approaches for datasets from the knee and hip joint region, which would be
thenceforth applied to all anatomical regions in our continuing development
of an ideal segmentation procedure for 3D fluoroscopic images.
