Accuracy of finite element model‐based multi‐organ deformable image registration
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: none
- Genre
- Candidate signal: MethodsConsensus signal: none
- Teacher disagreement score
- 0.855
- Threshold uncertainty score
- 0.381
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.313 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
As more pretreatment imaging becomes integrated into the treatment planning process and full three-dimensional image-guidance becomes part of the treatment delivery the need for a deformable image registration technique becomes more apparent. A novel finite element model-based multiorgan deformable image registration method, MORFEUS, has been developed. The basis of this method is twofold: first, individual organ deformation can be accurately modeled by deforming the surface of the organ at one instance into the surface of the organ at another instance and assigning the material properties that allow the internal structures to be accurately deformed into the secondary position and second, multi-organ deformable alignment can be achieved by explicitly defining the deformation of a subset of organs and assigning surface interfaces between organs. The feasibility and accuracy of the method was tested on MR thoracic and abdominal images of healthy volunteers at inhale and exhale. For the thoracic cases, the lungs and external surface were explicitly deformed and the breasts were implicitly deformed based on its relation to the lung and external surface. For the abdominal cases, the liver, spleen, and external surface were explicitly deformed and the stomach and kidneys were implicitly deformed. The average accuracy (average absolute error) of the lung and liver deformation, determined by tracking visible bifurcations, was 0.19 (s.d.: 0.09), 0.28 (s.d.: 0.12) and 0.17 (s.d.: 0.07) cm, in the LR, AP, and IS directions, respectively. The average accuracy of implicitly deformed organs was 0.11 (s.d.: 0.11), 0.13 (s.d.: 0.12), and 0.08 (s.d.: 0.09) cm, in the LR, AP, and IS directions, respectively. The average vector magnitude of the accuracy was 0.44 (s.d.: 0.20) cm for the lung and liver deformation and 0.24 (s.d.: 0.18) cm for the implicitly deformed organs. The two main processes, explicit deformation of the selected organs and finite element analysis calculations, require less than 120 and 495 s, respectively. This platform can facilitate the integration of deformable image registration into online image guidance procedures, dose calculations, and tissue response monitoring as well as performing multi-modality image registration for purposes of treatment planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Medical Physics
- Topic
- Medical Imaging Techniques and Applications
- Field
- Medicine
- Canadian institutions
- Princess Margaret Cancer CentreUniversity Health Network
- Funders
- Varian Medical Systems
- Keywords
- Image registrationFinite element methodDeformation (meteorology)Process (computing)Tracking (education)Computer visionPosition (finance)Surface (topology)Computer scienceArtificial intelligenceMedical imagingImage (mathematics)MathematicsGeometryPhysics
- Has abstract in OpenAlex
- yes