Bibliographic record
Abstract
The Sunnybrook Cardiac Data (SCD), also known as the 2009 Cardiac MR Left Ventricle Segmentation Challenge data, consist of 45 cine-MRI images from a mixed of patients and pathologies: healthy, hypertrophy, heart failure with infarction and heart failure without infarction. Subset of this data set was first used in the automated myocardium segmentation challenge from short-axis MRI, held by a MICCAI workshop in 2009. The whole complete data set is now available in the CAP database with public domain license. Classification There are four pathological groups in this data set, which were classified based on (K Alfakih et al., JMRI 2003) paper, i.e.: Heart failure with infarction (HF-I) group had ejection fraction (EF) < 40% and evidence of late gadolinium (Gd) enhancement. Heart failure without infarction (HF) group had EF < 40% and no late Gd enhancement. LV hypertrophy (HYP) group had normal EF (> 55%) and a ratio of left ventricular (LV) mass over body surface area is > 83 g/m2. Healthy (N) group had EF > 55% and no hypertrophy. The following table shows group statistics written as average (stddev) : Availability The Cardiac Atlas Project provides the dissemination of the Sunnybrook data by hosting them in the CAP databases. Finite element models (see supporting files section below) derived from these data are also provided. The whole complete data are available for any users, including the guest user account. License and attribution of these data set, including its derivatives, follows the Public Domain (CC0 1.0 Universal). If you are using this data in a publication, please cite the following reference: Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. “Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI.” The MIDAS Journal – Cardiac MR Left Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070 Data contributor Perry Radau – Sunnybrook Health Sciences Centre, Toronto, Canada. More information The original 2009 LV Segmentation Challenge webpage. Promotional poster for the 2009 LV Segmentation Challenge. The challenge results published in the MIDAS journal.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.509 | 0.292 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".