Sci-Fri AM: Imaging - 03: Automated Registration of X-Ray Mammograms and Magnetic Resonance Breast Images
Bibliographic record
Abstract
Breast cancer is a common and devastating form of cancer, with an estimated 22,700 new cases in 2009 in Canada alone. X-ray mammography is the most commonly used imaging technique for detection and diagnosis, while magnetic resonance imaging (MRI) is used in some challenging cases. Both modalities rely on different properties of the tissue to form images, and thus contribute different and complimentary information about the breast. However, due to geometric distortions during the acquisition processes, it is difficult to identify and compare the same anatomical location on both modalities. In this work, a method to register 2D mammograms to projection images of MRI volumes is presented. In order to compare the 3D MRI to the 2D mammogram, a “simulated mammogram” is formed from the MRI volume. Three anatomical landmarks on the surface of the breast are identified on each image and aligned to distort the general shape of the mammogram to match that of the MR projection image. Final registration is then achieved by iteratively applying a non-linear transformation to the mammogram until the mutual information of the two images is maximized. The registration method was tested on eight pairs of images from two volunteers (two mammographic views from each breast). Results from this small dataset are promising, with an average alignment error of 3.9%, measured as the difference in area between the two registered images. Future work will examine a larger dataset, including pathological cases, and quantification of internal alignment errors.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.039 |
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".