Toward Improving Safety in Neurosurgery with an Active Handheld Instrument

Abstract

Microsurgical procedures, such as petroclival meningioma resection, require careful surgical actions in order to remove tumor tissue, while avoiding brain and vessel damaging. Such procedures are currently performed under microscope magnification. Robotic tools are emerging in order to filter surgeons' unintended movements and prevent tools from entering forbidden regions such as vascular structures. The present work investigates the use of a handheld robotic tool (Micron) to automate vessel avoidance in microsurgery. In particular, we focused on vessel segmentation, implementing a deep-learning-based segmentation strategy in microscopy images, and its integration with a feature-based passive 3D reconstruction algorithm to obtain accurate and robust vessel position. We then implemented a virtual-fixture-based strategy to control the handheld robotic tool and perform vessel avoidance. Clay vascular phantoms, lying on a background obtained from microscopy images recorded during petroclival meningioma surgery, were used for testing the segmentation and control algorithms. When testing the segmentation algorithm on 100 different phantom images, a median Dice similarity coefficient equal to 0.96 was achieved. A set of 25 Micron trials of 80 s in duration, each involving the interaction of Micron with a different vascular phantom, were recorded, with a safety distance equal to 2 mm, which was comparable to the median vessel diameter. Micron's tip entered the forbidden region 24% of the time when the control algorithm was active. However, the median penetration depth was 16.9 μm, which was two orders of magnitude lower than median vessel diameter. Results suggest the system can assist surgeons in performing safe vessel avoidance during neurosurgical procedures.

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

“…Regarding further work, we will consider other approaches to database augmentation (e.g., increasing number of subjects) in order to improve the generalization capability of the network. One of the main improvements will be adjusting the architecture so that it could be used during brain surgery, classifying and accurately locating the tumor [49]. Detecting the tumors in the operating room should be performed in real-time and real-world conditions; thus, in that case, the improvement would also involve adapting the network to a 3D system [50].…”

Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better than Traditional Metrics in a Thoracic Cavity Segmentation Workflow

“…3 They may inform 3-D printed implant designs for orthopedists 4,5 or highlight posterior segment lesions [6][7][8] for ophthalmologists. They may help neurosurgeons spare microvessels, 9 outline catheters for radiation oncologists during MRI-guided brachytherapy, 10 or characterize vocal fold mobility for otorhinolaryngologists. 11 Dedicated imaging specialistsradiologists and pathologistsare likely to identify even more autosegmentation uses than clinicians whose primary clinical domain is not imaging.…”

Enhanced Vision to Improve Safety in Robotic Surgery

“…Intra-operative tissue classification plays a fundamental role in different clinical fields, such as laryngology [30,31], dermatology [32], oncology [33], ophthalmology [34], and neurosurgery [35]. Tissue classification is performed for diagnosis, treatment planning and execution, and for treatment outcome evaluation and follow up.…”