Evidence map›Paper›PMID 37162735›Full record

ArticleInternational journal of computer assisted radiology and surgery2023

3D US-CT/MRI registration for percutaneous focal liver tumor ablations.

Shuwei Xing, Joeana Cambranis Romero, Priyanka Roy, Derek W Cool, David Tessier, Elvis C S Chen, Terry M Peters, Aaron Fenster

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
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

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Global registration of kidneys in 3D ultrasound and CT images.International journal of computer assisted radiology and surgery · 2025
    Article
  4. Review
  5. Article
  6. 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

8 authors at 1 institution in 1 country.

Shuwei XingRobarts Research Institute, Western University, 100 Perth St., London, ON, N6A 5B7, Canada. xshuwei@uwo.ca.ORCID http://orcid.org/0000-0001-7734-2752
Joeana Cambranis RomeroRobarts Research Institute, Western University, 100 Perth St., London, ON, N6A 5B7, Canada.ORCID https://orcid.org/0000-0002-4779-1730
Priyanka RoyRobarts Research Institute, Western University, 100 Perth St., London, ON, N6A 5B7, Canada.
Derek W CoolDepartment of Medical Imaging, Western University, 100 Perth St., London, ON, N6A 5B7, Canada.
David TessierRobarts Research Institute, Western University, 100 Perth St., London, ON, N6A 5B7, Canada.
Elvis C S ChenRobarts Research Institute, Western University, 100 Perth St., London, ON, N6A 5B7, Canada.ORCID https://orcid.org/0000-0002-4198-3336
Terry M PetersRobarts Research Institute, Western University, 100 Perth St., London, ON, N6A 5B7, Canada.ORCID https://orcid.org/0000-0003-1440-7488
Aaron FensterRobarts Research Institute, Western University, 100 Perth St., London, ON, N6A 5B7, Canada.ORCID https://orcid.org/0000-0003-3525-2788
Western University · CA

Funding

CIHR 143232CIHR 154314
6 · The paper itself

Abstract

purposeUS-guided percutaneous focal liver tumor ablations have been considered promising curative treatment techniques. To address cases with invisible or poorly visible tumors, registration of 3D US with CT or MRI is a critical step. By taking advantage of deep learning techniques to efficiently detect representative features in both modalities, we aim to develop a 3D US-CT/MRI registration approach for liver tumor ablations.

methodsFacilitated by our nnUNet-based 3D US vessel segmentation approach, we propose a coarse-to-fine 3D US-CT/MRI image registration pipeline based on the liver vessel surface and centerlines. Then, phantom, healthy volunteer and patient studies are performed to demonstrate the effectiveness of our proposed registration approach.

resultsOur nnUNet-based vessel segmentation model achieved a Dice score of 0.69. In healthy volunteer study, 11 out of 12 3D US-MRI image pairs were successfully registered with an overall centerline distance of 4.03±2.68 mm. Two patient cases achieved target registration errors (TRE) of 4.16 mm and 5.22 mm.

conclusionWe proposed a coarse-to-fine 3D US-CT/MRI registration pipeline based on nnUNet vessel segmentation models. Experiments based on healthy volunteers and patient trials demonstrated the effectiveness of our registration workflow. Our code and example data are publicly available in this r epository.

Indexed as

Liver NeoplasmsTomography, X-Ray ComputedHumansImage Processing, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance Imaging3D USDeep learningImage registrationLiver ablations

Identifiers

PMID37162735
OpenAlexW4376121429

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.