Purpose: Automating fiducial detection and localization in the patient’s pre-operative images can lead to better registration accuracy, reduced human errors, and shorter intervention time. Most current approaches are optimized for a single marker type, mainly spherical adhesive markers. A fully automated algorithm is proposed and evaluated for screw and spherical titanium fiducials, typically used in high-accurate frameless surgical navigation.
Approach: The algorithm builds on previous approaches with morphological functions and pose estimation algorithms. A 3D convolutional neural network (CNN) is proposed for the fiducial classification task and evaluated for both traditional closed-set and emerging open-set classifiers. A proposed digital ground-truth experiment, with cone-beam computed tomography (CBCT) imaging software, is performed to determine the localization accuracy of the algorithm. The localized fiducial positions in the CBCT images by the presented algorithm were compared to the actual known positions in the virtual phantom models. The difference represents the fiducial localization error (FLE).
Results: A total of 241 screws, 151 spherical fiducials, and 1550 other structures are identified with the best true positive rate 95.9% for screw and 99.3% for spherical fiducials at 8.7% and 3.4% false positive rate, respectively. The best achieved FLE mean and its standard deviation for a screw and spherical marker are 58 (14) and 14 ( 6 ) μm, respectively.
Conclusions: Accurate marker detection and localization were achieved, with spherical fiducials being superior to screws. Large marker volume and smaller voxel size yield significantly smaller FLEs. Attenuating noise by mesh smoothing has a minor effect on FLE. Future work will focus on expanding the CNN for image segmentation.
Patient-to-image registration is currently mostly done in a semi-automated way. Fully automating this task can enhance surgical workflow and reduce human errors. Here, we present a novel solution with a nasal stent utilized to minimally invasively position spherical fiducials inside the nasopharynx and aimed for neurosurgery with electromagnetic navigation. The assembly was deployed into the pharyngeal region of a human specimen. The spherical fiducials are automatically detected in anatomy by integrated magnetic sensors while in preoperative imagery with a proposed u-net deep network. Temporal dislocation of the markers was measured interdaily after two brainstem procedures with CT imaging follow-ups. The u-net was trained to differentiate markers from other structures located in CT images. The ground-truth data was created from 22 CTs of phantoms, cadavers and swine. The dataset is split into 12, 5 and 5 for training, validation and testing, respectively. The dice coefficient was used as a similarity measure. The fiducial registration error resulted in 0.53±0.1 mm and 0.44±0.04 mm for the first and the second procedure, respectively. The fiducial positions deviated from the intraday baseline with the mean ± standard deviation 0.29±0.20 mm and 1.32±0.30 mm. The dice coefficient was 0.976, 0.878 and 0.764 during the training, validation and testing. The nasopharyngeal stent shows a potential for a stable marker fixation. The u-net can be adequately employed to segment titanium spherical fiducials.
KEYWORDS: Image segmentation, Computed tomography, Skull, Image registration, Surgery, 3D image processing, Detection and tracking algorithms, 3D modeling, Head
Patient-to-image registration is a key step for guidance in computer-assisted surgery during interventions like cochlear implant or deep brain stimulation surgeries. Automatizing fiducial detection and localization in pre-operative images of the patient can lead to better registration accuracy, reduced human errors and shorter intervention time. We present an algorithm that builds on earlier approaches with morphological functions and pose estimation algorithms. A Convolutional Neural Network is proposed for the fiducial classification task. A digital experiment, with cone-beam CT imaging software, is performed to determine the accuracy of the algorithm. The inputs to this software are virtual phantoms represented as 3D surface models (meshes) of skull and (screw and spherical) fiducial markers combined with specific imaging parameters like material properties, detector resolution, etc. The software generates realistic CT images for establishing a ground-truth measure to validate the algorithm. The localized fiducial positions in the image by the presented algorithm were compared to the actual known positions in the phantom models. The difference represents the fiducial localization error (FLE). Validation data sets with different slice thicknesses contain screws and spherical markers of different dimensions. The achieved FLE mean and its standard deviation for a screw and spherical marker are 58 (14) μm and 14 (6) μm, respectively. Large marker volume and smaller voxel size yield smaller FLEs. Furthermore, we found that attenuating noise by mesh smoothing has a minor effect on localization accuracy.
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