Evidence map›Paper›PMID 39953355›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

Leveraging deep learning for nonlinear shape representation in anatomically parameterized statistical shape models.

Behnaz Gheflati, Morteza Mirzaei, Sunil Rottoo, Hassan Rivaz

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Behnaz GheflatiDepartment of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada. b_ghefla@encs.concordia.ca.ORCID http://orcid.org/0009-0003-4683-3374
Morteza MirzaeiThink Surgical Inc., Montreal, QC, Canada.
Sunil RottooThink Surgical Inc., Montreal, QC, Canada.
Hassan RivazDepartment of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeStatistical shape models (SSMs) are widely used for morphological assessment of anatomical structures. However, a key limitation is the need for a clear relationship between the model's shape coefficients and clinically relevant anatomical parameters. To address this limitation, this paper proposes a novel deep learning-based anatomically parameterized SSM (DL-ANAT

methodsOur approach utilizes a multilayer perceptron model trained on a synthetic femoral bone population to learn the nonlinear mapping between anatomical measurements and shape parameters. The trained model is then fine-tuned on a real bone dataset. We compare the performance of DL-ANAT

resultsWhen applied to a previously unseen femoral bone dataset, DL-ANAT

conclusionThe proposed DL-ANAT

Indexed as

Deep LearningFemurImaging, Three-DimensionalModels, StatisticalTomography, X-Ray ComputedHumansNonlinear DynamicsAnatomically parameterized modelsDeep learningFemur structure analysisNonlinear shape representationStatistical shape models

Identifiers

PMID39953355

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.