Patient-specific functional liver segments based on centerline classification of the hepatic and portal veins
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Couinaud's liver segment classification has been widely adopted for liver surgery planning, yet its rigid anatomical boundaries often fail to align precisely with individual patient anatomy. This study proposes a novel patient-specific liver segmentation method based on detailed classification of hepatic and portal veins to improve anatomical adherence and clinical relevance. METHODS: Our proposed method involves two key stages: (1) surgeons annotate vascular endpoints on 3D models of hepatic and portal veins, from which vessel centerlines are computed; and (2) liver segments are calculated by assigning voxel labels based on proximity to these vascular centerlines. The accuracy and clinical applicability of our Hepatic and Portal Vein-based Classification (HPVC) were compared with conventional Plane-Based Classification (PBC), Portal Vein-Based Classification (PVC), and an automated deep learning method (nnU-Net) using volumetric measurements, Dice similarity scores, and expert evaluations. RESULTS: HPVC demonstrated superior anatomical conformity compared to traditional methods, especially in complex segments like 5 and 8, providing segmentations more reflective of actual vascular territories. Volumetric analysis revealed significant discrepancies among the methods, particularly with nnU-Net generally producing larger segment volumes. HPVC consistently achieved higher surgeon-rated scores in patient-specific anatomical adherence, perfusion region assessment, and accuracy in surgical planning compared to PBC, PVC, and nnU-Net. CONCLUSION: the open-source platform 3D Slicer significantly enhances its accessibility and usability.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
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)
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 it