An automatic progressive chromosome segmentation approach using deep learning with traditional image processing-Reference-Cited by-同舟云学术

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An automatic progressive chromosome segmentation approach using deep learning with traditional image processing

Published:2023-09-28 Issue:1 Volume:62 Page:207-223
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Chang Ling,Wu Kaijie^ORCID,Cheng Hao,Gu Chaocheng,Zhao Yudi,Chen Cailian

Funder

National Key Scientific Instrument and Equipment Development Projects of China

National Major Scientific Research Instrument Development Project

Science and Technology Commission of Shanghai Municipality under Grant

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11517-023-02896-x.pdf

Reference34 articles.

1. Tjio JH, Levan A (1956) The chromosome number in man. Hereditas 42:1–6

2. Conference D (1960) A proposed standard system of nomenclature of human mitotic chromosomes. Lancet 1:1063–1065

3. O’Connor C (2008) Karyotyping for chromosomal abnormalities. Nature Educ 1:27

4. Liu X, Fu L, Lin CW et al (2022) SRAS-net: low-resolution chromosome image classification based on deep learning[J]. IET Syst Biol 16(3–4):85–97

5. Natarajan AT (2002) Chromosome aberrations: past, present and future. Mutat Res/Fund Mol Mech Mutagen 504(1):3–16

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