A comparison of hepatic segmental anatomy as revealed by cross‐sections and MPR CT imaging
Bibliographic record
Abstract
To compare the areas of human liver horizontal sections with computed tomography (CT) images and to evaluate whether the subsegments determined by CT are consistent with the actual anatomy. Six human cadaver livers were made into horizontal slices with multislice spiral CT three-dimensional (3D) reconstruction was used during infusion process. Each liver segment was displayed using different color, and 3D images of the portal and hepatic vein were reconstructed. Each segmental area was measured on CT-reconstructed images, which were compared with the actual area on the sections of the same liver. The measurements were performed at four key levels namely: (1) the three hepatic veins, (2) the left, and (3) the right branch of portal vein (PV), and (4) caudal to the bifurcation of the PV. By dividing the sum of these areas by the total area of the liver, the authors got the percentage of the incorrectly determined subsegmental areas. In addition to these percentage values, the maximum distances of the radiologically determined intersegmental boundaries from the true anatomic boundaries were measured. On the four key levels, an average of 28.64 ± 10.26% of the hepatic area of CT images was attributed to an incorrect segment. The mean-maximum error between artificial segments on images and actual anatomical segments was 3.81 ± 1.37 cm. The correlation between radiological segmenting method and actual anatomy was poor. The hepatic segments being divided strictly according to the branching point of the PV could be more informative during liver segmental resection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.000 |
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".