1 The aim of this retrospective case study2 was to validate the accuracy of the use 3 of protractors in a virtual reality environment. 4 This was a blind study in which the surgeon's placement 5 in VR was compared to the actual positioning 6 used in surgery. 7 Currently, optimal visualization of brain aneurysms8 and other intracranial vascular malformations 9 for treatment or planning requires 3D rotational angiograms 10 to be taken at the beginning of the procedure.
11 This exposes the patient to radiation 12 and increases procedural times. 13 Segmentation in virtual reality boosts surgeon confidence14 and increase understanding of complex surgical cases. 15 The human body exists in a 3D space and thus viewing it 16 in 3D space is much more indicative of pathological anatomy 17 than viewing a 2D CT or MRI.
18 These findings impact research and medicine, 19 reducing planning time for determining 20 the imaging equipment angle in the OR.21 Currently, patients must be scanned with a 360 spin 22 so C-arm position can be determined. 23 We believe that we can reduce the time the patient 24 is sedated and their radiation exposure by offering 25 the suggestions for C-arm positioning prior to surgery. 26 Segmentation is the act of recreating 3D models27 of patient-specific anatomy from medical images.
28 It is the most critical step, yet it is resource-heavy 29 in both software requirements and human expertise. 30 As such, our primary research focus 31 is automating segmentation, 32 utilizing machine learning techniques 33 to decrease the barrier to entry 34 for deploying this technology at scale.
