Evidence map›Paper›PMID 38293979›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2024

[Multi-phase CT synthesis-assisted segmentation of abdominal organs].

P Huang, L Zhong, K Zheng, Z Chen, R Xiao, X Quan, W Yang

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
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Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

P HuangSchool of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
L ZhongSchool of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
K ZhengSchool of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
Z ChenSchool of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
R XiaoSchool of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
X QuanDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
W YangSchool of Biomedical Engineering, Southern Medical University//Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo propose a method for abdominal multi-organ segmentation assisted by multi-phase CT synthesis.

methodsMulti-phase CT synthesis for synthesizing high-quality CT images was used to increase the information details for image segmentation. A transformer block was introduced to help to capture long-range semantic information in cooperation with perceptual loss to minimize the differences between the real image and synthesized image.

resultsThe model was trained using multi-phase CT dataset of 526 total cases from Nanfang Hospital. The mean maximum absolute error (MAE) of the synthesized non-contrast CT, venous phase contrast- enhanced CT (CECT), and delay phase CECT images from arterial phase CECT was 19.192±3.381, 20.140±2.676 and 22.538±2.874, respectively, which were better than those of images synthesized using other methods. Validation of the multi-phase CT synthesis-assisted abdominal multi-organ segmentation method showed an average dice coefficient of 0.847 for the internal validation set and 0.823 for the external validation set.

conclusionThe propose method is capable of synthesizing high-quality multi-phase CT images to effectively reduce the errors in registration between different phase CT images and improve the performance for segmentation of 13 abdominal organs.

Indexed as

Image Processing, Computer-AssistedTomography, X-Ray Computedabdominal multi-organ segmentationadversarial generative networksmulti-phase CT synthesisTransformer

Identifiers

PMID38293979
PMCPMC10878894

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