Evidence map›Paper›PMID 28420871›Full record

ArticleScientific reports2017

Interactive Outlining of Pancreatic Cancer Liver Metastases in Ultrasound Images.

Jan Egger, Dieter Schmalstieg, Xiaojun Chen, Wolfram G Zoller, Alexander Hann

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jan EggerInstitute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Austria. egger@tugraz.at.
Dieter SchmalstiegInstitute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010, Graz, Austria.
Xiaojun ChenShanghai Jiao Tong University, School of Mechanical Engineering, 800 Dong Chuan Road, Shanghai, 200240, China. xiaojunchen@163.com.
Wolfram G ZollerDepartment of Internal Medicine and Gastroenterology, Katharinenhospital, Stuttgart, Germany.
Alexander HannDepartment of Internal Medicine I, Ulm University, Ulm, Germany. Alexander.Hann@uniklinik-ulm.de.ORCID http://orcid.org/0000-0001-8035-3559

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultrasound (US) is the most commonly used liver imaging modality worldwide. Due to its low cost, it is increasingly used in the follow-up of cancer patients with metastases localized in the liver. In this contribution, we present the results of an interactive segmentation approach for liver metastases in US acquisitions. A (semi-) automatic segmentation is still very challenging because of the low image quality and the low contrast between the metastasis and the surrounding liver tissue. Thus, the state of the art in clinical practice is still manual measurement and outlining of the metastases in the US images. We tackle the problem by providing an interactive segmentation approach providing real-time feedback of the segmentation results. The approach has been evaluated with typical US acquisitions from the clinical routine, and the datasets consisted of pancreatic cancer metastases. Even for difficult cases, satisfying segmentations results could be achieved because of the interactive real-time behavior of the approach. In total, 40 clinical images have been evaluated with our method by comparing the results against manual ground truth segmentations. This evaluation yielded to an average Dice Score of 85% and an average Hausdorff Distance of 13 pixels.

Indexed as

AlgorithmsHumansImage Processing, Computer-AssistedLiver NeoplasmsPancreatic NeoplasmsUltrasonography

Identifiers

PMID28420871
PMCPMC5429849

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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.