A bi-directional segmentation method for prostate ultrasound images under semantic constraints.

Zexiang Li, Wei Du, Yongtao Shi, Wei Li, Chao Gao

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Zexiang LiCollege of Electrical Engineering and New Energy, China Three Gorges University, Yichang, Hubei, 443002, China.

Wei DuCollege of Computer and Information Technology, China Three Gorges University, Yichang, Hubei, 443002, China.

Yongtao ShiCollege of Computer and Information Technology, China Three Gorges University, Yichang, Hubei, 443002, China. ytshi@ctgu.edu.cn.

Wei LiCollege of Computer and Information Technology, China Three Gorges University, Yichang, Hubei, 443002, China.

Chao GaoCollege of Computer and Information Technology, China Three Gorges University, Yichang, Hubei, 443002, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to the lack of sufficient labeled data for the prostate and the extensive and complex semantic information in ultrasound images, accurately and quickly segmenting the prostate in transrectal ultrasound (TRUS) images remains a challenging task. In this context, this paper proposes a solution for TRUS image segmentation using an end-to-end bidirectional semantic constraint method, namely the BiSeC model. The experimental results show that compared with classic or popular deep learning methods, this method has better segmentation performance, with the Dice Similarity Coefficient (DSC) of 96.74% and the Intersection over Union (IoU) of 93.71%. Our model achieves a good balance between actual boundaries and noise areas, reducing costs while ensuring the accuracy and speed of segmentation.

Indexed as

ProstateProstatic NeoplasmsSemanticsUltrasonographyAlgorithmsDeep LearningHumansImage Interpretation, Computer-AssistedImage Processing, Computer-AssistedMale

Identifiers

PMID38778034

DOI10.1038/s41598-024-61238-5

PMCPMC11634890

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LicenceCC BY

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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.