Evidence map›Paper›PMID 35257285›Full record

ArticleInternational journal of computer assisted radiology and surgery2022

Toward automated segmentation for acute ischemic stroke using non-contrast computed tomography.

Shih-Yen Lin, Pi-Ling Chiang, Peng-Wen Chen, Li-Hsin Cheng, Meng-Hsiang Chen, Pei-Chun Chang, Wei-Che Lin, Yong-Sheng Chen

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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Shih-Yen Lin *Department of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan.
Pi-Ling Chiang *Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 123 Ta-Pei Road, Niao-Sung, Kaohsiung, 83305, Taiwan.
Peng-Wen ChenInstitute of Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Li-Hsin ChengDepartment of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan.
Meng-Hsiang ChenDepartment of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 123 Ta-Pei Road, Niao-Sung, Kaohsiung, 83305, Taiwan.
Pei-Chun ChangDepartment of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan.
Wei-Che LinDepartment of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 123 Ta-Pei Road, Niao-Sung, Kaohsiung, 83305, Taiwan. alex@cgmh.org.tw.
Yong-Sheng ChenDepartment of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan. yschen@nycu.edu.tw.ORCID http://orcid.org/0000-0002-5581-850X

Funding

Kaohsiung Chang Gung Memorial Hospital CMRPG8H0951Kaohsiung Chang Gung Memorial Hospital CMRPG8K0131Ministry of Science and Technology, Taiwan MOST 109-2321-B-009-007
6 · The paper itself

Abstract

purposeNon-contrast computed tomography (NCCT) is a first-line imaging technique for determining treatment options for acute ischemic stroke (AIS). However, its poor contrast and signal-to-noise ratio limit the diagnosis accuracy for radiologists, and automated AIS lesion segmentation using NCCT also remains a challenge. In this paper, we propose R2U-RNet, a novel model for AIS lesion segmentation using NCCT.

methodsWe used an in-house retrospective NCCT dataset with 261 AIS patients with manual lesion segmentation using follow-up diffusion-weighted images. R2U-RNet is based on an R2U-Net backbone with a novel residual refinement unit. Each input image contains two image channels from separate preprocessing procedures. The proposed model incorporates multiscale focal loss to mitigate the class imbalance problem and to leverage the importance of different levels of details. A proposed noisy-label training scheme is utilized to account for uncertainties in the manual annotations.

resultsThe proposed model outperformed several iconic segmentation models in AIS lesion segmentation using NCCT, and our ablation study demonstrated the efficacy of the proposed model. Statistical analysis of segmentation performance revealed significant effects of regional stroke occurrence and side of the stroke, suggesting the importance of region-specific information for automated segmentation, and the potential influence of the hemispheric difference in clinical data.

conclusionThis study demonstrated the potentials of R2U-RNet model for automated NCCT AIS lesion segmentation. The proposed model can serve as a tool for accelerating AIS diagnoses and improving the treatment quality of AIS patients.

Indexed as

Ischemic StrokeStrokeHumansRetrospective StudiesSignal-To-Noise RatioTomography, X-Ray ComputedAcute ischemic strokeDeep learningImage segmentationNon-contrast computed tomography

Identifiers

PMID35257285

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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.