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- W4226137411 abstract "Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite a large number of survey papers already present in this field, most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 145 research papers published after 2017. Each paper is analyzed and commented on from both the methodology and application perspective. We categorized the papers in (i) fetal standard-plane detection, (ii) anatomical-structure analysis, and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice." @default.
- W4226137411 created "2022-05-05" @default.
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- W4226137411 date "2022-01-28" @default.
- W4226137411 modified "2023-09-24" @default.
- W4226137411 title "A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis" @default.
- W4226137411 doi "https://doi.org/10.48550/arxiv.2201.12260" @default.
- W4226137411 hasPublicationYear "2022" @default.
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