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Record W2021839708 · doi:10.1118/1.3182294

MO‐FF‐A3‐06: Preliminary Feasibility Study: Modeling 3D Deformations of the Prostate From Whole‐Mount Histology to in Vivo MRI

2009· article· en· W2021839708 on OpenAlexaff
Andrea McNiven, Joanne Moseley, DL Langer, MA Haider, K. Brock

Bibliographic record

VenueMedical Physics · 2009
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIn vivoProstateMagnetic resonance imagingEx vivoHistologyProstatectomyContouringImage registrationFixation (population genetics)Biomedical engineeringNuclear medicineMedicineComputer sciencePathologyRadiologyArtificial intelligenceBiologyCancerImage (mathematics)

Abstract

fetched live from OpenAlex

Purpose : To investigate the accuracy of a 3D biomechanical model‐based deformation algorithm (MORFEUS) in modeling the prostate deformation that occurs between in vivo magnetic resonance imaging (MRI) and identification of the tumor on whole‐mount histology. Method and Materials : Three image sets were acquired for 10 patients: 1) in vivo T2‐weighted MR images acquired prior to prostatectomy, 2) ex vivo T2‐weighted MR images, and 3) digital images of the histological slices, rigidly registered to construct a 3D volumetric image. All three images sets were imported into the radiation treatment planning system for contouring. The entire prostate gland, the peripheral zone and central gland were contoured. The prostate was converted into a finite element model, where each zone was assigned the appropriate material property. Naturally occurring structural and morphological features ( e.g. urethra) were identified as verification points in the in vivo, ex vivo , and histological images, for quantification of the accuracy of the deformable registration. MORFEUS was used to model the deformations that occur due to excision and fixation either directly, deforming histology to in vivo MRI, or using a two‐step process, histology to in vivo MRI via an intermediate step, using ex vivo MRI. Results : Initial analysis has been completed for a subset of the patients. Uncertainties following rigid registration alone exceeded 8.0mm. No significant improvements were observed when including the intermediate deformation step. The average absolute error following deformable registration, based on the verification points, was 1.3, 1.2, and 1.9mm in the left/right, anterior/posterior, and superior/inferior directions, respectively. This error is smaller than the 3 mm image slice thickness. Conclusions : Substantial deformation confounds the ability to compare histology with in vivo imaging. Deformable registration using MORFEUS can be used to resolve the deformation, enabling quantitative evaluation of in vivo imaging based on histology as a gold standard for tumor definition.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.015
GPT teacher head0.258
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2009
Admission routes1
Has abstractyes

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