<title>Software components for medical image visualization and surgical planning</title>
Bibliographic record
Abstract
Purpose: The development of new applications in medical image visualization and surgical planning requires the completion of many common tasks such as image reading and re-sampling, segmentation, volume rendering, and surface display. Intra-operative use requires an interface to a tracking system and image registration, and the application requires basic, easy to understand user interface components. Rapid changes in computer and end-application hardware, as well as in operating systems and network environments make it desirable to have a hardware and operating system as an independent collection of reusable software components that can be assembled rapidly to prototype new applications. Methods: Using the OpenGL based Visualization Toolkit as a base, we have developed a set of components that implement the above mentioned tasks. The components are written in both C++ and Python, but all are accessible from Python, a byte compiled scripting language. The components have been used on the Red Hat Linux, Silicon Graphics Iris, Microsoft Windows, and Apple OS X platforms. Rigorous object-oriented software design methods have been applied to ensure hardware independence and a standard application programming interface (API). There are components to acquire, display, and register images from MRI, MRA, CT, Computed Rotational Angiography (CRA), Digital Subtraction Angiography (DSA), 2D and 3D ultrasound, video and physiological recordings. Interfaces to various tracking systems for intra-operative use have also been implemented. Results: The described components have been implemented and tested. To date they have been used to create image manipulation and viewing tools, a deep brain functional atlas, a 3D ultrasound acquisition and display platform, a prototype minimally invasive robotic coronary artery bypass graft planning system, a tracked neuro-endoscope guidance system and a frame-based stereotaxy neurosurgery planning tool. The frame-based stereotaxy module has been licensed and certified for use in a commercial image guidance system. Conclusions: It is feasible to encapsulate image manipulation and surgical guidance tasks in individual, reusable software modules. These modules allow for faster development of new applications. The strict application of object oriented software design methods allows individual components of such a system to make the transition from the research environment to a commercial one.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.292 | 0.300 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".