Evidence map›Paper›PMID 35601480›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation.

Seung Yeon Shin, Sungwon Lee, Ronald M Summers

Abstract read
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In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Low-Resource Adversarial Domain Adaptation for Cross-Modality Nucleus Detection.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022
    Article
  5. Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022
    Article
  6. GRAPH-BASED SMALL BOWEL PATH TRACKING WITH CYLINDRICAL CONSTRAINTS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022
    Article
  7. A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans.Proceedings of SPIE--the International Society for Optical Engineering
    Article
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

3 authors.

Seung Yeon ShinImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Sungwon LeeImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Ronald M SummersImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.

Funding

Computer Aided Detection for Radiologic ImagesZ01CL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2003 to 2008
$41k
Intramural NIH HHS Z01 CL040004
6 · The paper itself

Abstract

We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentangle intensity and non-intensity features within a unique two-stream auto-encoding architecture, and selectively adapt the non-intensity features that are believed to be more transferable across domains. The segmentation prediction is performed by aggregating the disentangled features. We evaluated our method using intravenous contrast-enhanced abdominal CT scans with and without oral contrast, which are used as source and target domains, respectively. The proposed method showed clear improvements in terms of three different metrics compared to other domain adaptation methods that are without the feature disentanglement. The method brings small bowel segmentation closer to clinical application.

Indexed as

Abdominal computed tomographyFeature disentanglementSmall bowel segmentationUnsupervised domain adaptation

Identifiers

PMID35601480
PMCPMC9115845

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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.