Role of the Pretraining and the Adaptation data sizes for low-resource real-time MRI video segmentation

Real-time Magnetic Resonance Imaging (rtMRI) is frequently used in speech production studies as it provides a complete view of the vocal tract during articulation. This study investigates the effectiveness of rtMRI in analyzing vocal tract movements by employing the SegNet and UNet models for Air-Tissue Boundary (ATB)segmentation tasks. We conducted pretraining of a few base models using increasing numbers of subjects and videos, to assess performance on two datasets. First, consisting of unseen subjects with unseen videos from the same data source, achieving 0.33% and 0.91% (Pixel-wise Classification Accuracy (PCA) and Dice Coefficient respectively) better than its matched condition. Second, comprising unseen videos from a new data source, where we obtained an accuracy of 99.63% and 98.09% (PCA and Dice Coefficient respectively) of its matched condition performance. Here, matched condition performance refers to the performance of a model trained only on the test subjects which was set as a benchmark for the other models. Our findings highlight the significance of fine-tuning and adapting models with limited data. Notably, we demonstrated that effective model adaptation can be achieved with as few as 15 rtMRI frames from any new dataset.

Generational Frameshifts in Technology: Computer Science and Neurosurgery, The VR Use Case

We are at a unique moment in history where there is a confluence of technologies which will synergistically come together to transform the practice of neurosurgery. These technological transformations will be all-encompassing, including improved tools and methods for intraoperative performance of neurosurgery, scalable solutions for asynchronous neurosurgical training and simulation, as well as broad aggregation of operative data allowing fundamental changes in quality assessment, billing, outcome measures, and dissemination of surgical best practices. The ability to perform surgery more safely and more efficiently while capturing the operative details and parsing each component of the operation will open an entirely new epoch advancing our field and all surgical specialties. The digitization of all components within the operating room will allow us to leverage the various fields within computer and computational science to obtain new insights that will improve care and delivery of the highest quality neurosurgery regardless of location. The democratization of neurosurgery is at hand and will be driven by our development, extraction, and adoption of these tools of the modern world. Virtual reality provides a good example of how consumer-facing technologies are finding a clear role in industry and medicine and serves as a notable example of the confluence of various computer science technologies creating a novel paradigm for scaling human ability and interactions. The authors describe the technology ecosystem that has come and highlight a myriad of computational and data sciences that will be necessary to enable the operating room of the near future.