Visual guidance for auditory brainstem implantation with modular software design

Regodic, Milovan; Freysinger, Wolfgang

doi:10.1515/cdbme-2020-0044

Cited by 3 publications

(10 citation statements)

References 6 publications

(6 reference statements)

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“…2.5), an IGS system was utilized. The system is developed in-house and follows modular software design principles [13].…”

Section: Surgical Navigationmentioning

confidence: 99%

“…The green-colored virtual cues encode the pointer while blue-colored the fixed target. (Images a/b/d appeared previously in [13] are used by permission)…”

Section: Pose Estimation Of Surgical Instrumentsmentioning

confidence: 99%

“…This paper is an extended version of the contribution presented at the CARS / CURAC 2020 conferences that were focused to demonstrate the software design principles and included preliminary results [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Visual display for surgical targeting: concepts and usability study

et al. 2021

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Purpose Interactive image-guided surgery technologies enable accurate target localization while preserving critical nearby structures in many surgical interventions. Current state-of-the-art interfaces largely employ traditional anatomical cross-sectional views or augmented reality environments to present the actual spatial location of the surgical instrument in preoperatively acquired images. This work proposes an alternative, simple, minimalistic visual interface intended to assist during real-time surgical target localization. Methods The estimated 3D pose of the interventional instruments and their positional uncertainty are intuitively presented in a visual interface with respect to the target point. A usability study with multidisciplinary participants evaluates the proposed interface projected in surgical microscope oculars against cross-sectional views. The latter was presented on a screen both stand-alone and combined with the proposed interface. The instruments were electromagnetically navigated in phantoms. Results The usability study demonstrated that the participants were able to detect invisible targets marked in phantom imagery with significant enhancements for localization accuracy and duration time. Clinically experienced users reached the targets with shorter trajectories. The stand-alone and multi-modal versions of the proposed interface outperformed cross-sectional views-only navigation in both quantitative and qualitative evaluations. Conclusion The results and participants’ feedback indicate potential to accurately navigate users toward the target with less distraction and workload. An ongoing study evaluates the proposed system in a preclinical setting for auditory brainstem implantation.

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“…2.5), an IGS system was utilized. The system is developed in-house and follows modular software design principles [13].…”

Section: Surgical Navigationmentioning

confidence: 99%

“…The green-colored virtual cues encode the pointer while blue-colored the fixed target. (Images a/b/d appeared previously in [13] are used by permission)…”

Section: Pose Estimation Of Surgical Instrumentsmentioning

confidence: 99%

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Visual display for surgical targeting: concepts and usability study

et al. 2021

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“…The visual display (introduced in [9,10]) indicates the displacements between the current position of the tracked instrument tip and the designed target point using intuitive, minimalistic, and semi-transparent visual clues superimposed onto the video of the actual surgical site. The three axes of the instrument were encoded with respect to the observer's viewpoint: the z-axis runs longitudinally along the instrument tip while the x − y axes form a plane perpendicular to it, Fig.…”

Section: Visual Display For Surgical Targetingmentioning

confidence: 99%

“…This work evaluates a recently proposed visual-spatial display for surgical targeting aimed to assist, quantify and verify ABI placement [9,10]. The proposed system coupled with an innovative nasopharyngeal registration allows reaching accurately the stored position of the ABI determined with the placement electrode.…”

Section: Introductionmentioning

confidence: 99%

Novel microscope-based visual display and nasopharyngeal registration for auditory brainstem implantation: a feasibility study in an ex vivo model

et al. 2021

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Purpose An auditory brainstem implant (ABI) represents an alternative for patients with profound hearing loss who are constrained from receiving a cochlear implant. The positioning of the ABI electrode influences the patient’s auditory capacity and, therefore, quality of life and is challenging even with available intraoperative electrophysiological monitoring. This work aims to provide and assess the feasibility of visual-spatial assistance for ABI positioning. Methods The pose of the forceps instrument that grasps the electrode was electromagnetically navigated and interactively projected in the eyepieces of a surgical microscope with respect to a target point. Intraoperative navigation was established with an experimental technique for automated nasopharyngeal patient registration. Two ABI procedures were completed in a human specimen head. Results An intraoperative usability study demonstrated lower localization error when using the proposed visual display versus standard cross-sectional views. The postoperative evaluations of the preclinical study showed that the center of the electrode was misplaced to the planned position by 1.58 mm and 3.16 mm for the left and the right ear procedure, respectively. Conclusion The results indicate the potential to enhance intraoperative feedback during ABI positioning with the presented system. Further improvements consider estimating the pose of the electrode itself to allow for better orientation during placement.

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Automated fiducial marker detection and localization in volumetric computed tomography images: a three-step hybrid approach with deep learning

2021

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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 ð6Þ 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.

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Visual guidance for auditory brainstem implantation with modular software design

Cited by 3 publications

References 6 publications

Visual display for surgical targeting: concepts and usability study

Visual display for surgical targeting: concepts and usability study

Novel microscope-based visual display and nasopharyngeal registration for auditory brainstem implantation: a feasibility study in an ex vivo model

Automated fiducial marker detection and localization in volumetric computed tomography images: a three-step hybrid approach with deep learning

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