DaCSeg: Divide and conquer for accurate overlapping chromosome instance segmentation in metaphase cell images

Fan, Xinyu; Liu, Hui; Zheng, Hongdang; Zhai, Jingfang; Zhang, Lin

doi:10.1016/j.bspc.2023.105880

Biomedical Signal Processing and Control

2024

DOI: 10.1016/j.bspc.2023.105880

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DaCSeg: Divide and conquer for accurate overlapping chromosome instance segmentation in metaphase cell images

Xinyu Fan,

Hui Liu,

Hongdang Zheng

et al.

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2024

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Cited by 2 publications

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Chromosome segmentation and classification: an updated review

Somasundaram,

Madian,

Goh

et al. 2024

Knowl Inf Syst

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Chromosome segmentation and classification: an updated review

Somasundaram,

Madian,

Goh

et al. 2024

Knowl Inf Syst

View full text Add to dashboard Cite

State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues

Krikid,

Rositi,

Vacavant

2024

J. Imaging

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Microscopic image segmentation (MIS) is a fundamental task in medical imaging and biological research, essential for precise analysis of cellular structures and tissues. Despite its importance, the segmentation process encounters significant challenges, including variability in imaging conditions, complex biological structures, and artefacts (e.g., noise), which can compromise the accuracy of traditional methods. The emergence of deep learning (DL) has catalyzed substantial advancements in addressing these issues. This systematic literature review (SLR) provides a comprehensive overview of state-of-the-art DL methods developed over the past six years for the segmentation of microscopic images. We critically analyze key contributions, emphasizing how these methods specifically tackle challenges in cell, nucleus, and tissue segmentation. Additionally, we evaluate the datasets and performance metrics employed in these studies. By synthesizing current advancements and identifying gaps in existing approaches, this review not only highlights the transformative potential of DL in enhancing diagnostic accuracy and research efficiency but also suggests directions for future research. The findings of this study have significant implications for improving methodologies in medical and biological applications, ultimately fostering better patient outcomes and advancing scientific understanding.

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DaCSeg: Divide and conquer for accurate overlapping chromosome instance segmentation in metaphase cell images

Cited by 2 publications

References 22 publications

Chromosome segmentation and classification: an updated review

Chromosome segmentation and classification: an updated review

State-of-the-Art Deep Learning Methods for Microscopic Image Segmentation: Applications to Cells, Nuclei, and Tissues

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