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Cited by 24Medical Image Segmentation TechniquesAdvanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

A novel framework for segmentation of small targets in medical images

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Zhao, Longxuan, Wang, Tao, Chen, Yuanbin, Zhang, Xinlin, Tang, Hui, Lin, Fuxin, Li, Chunwang, Li, Qixuan +3 more·2025·Scientific Reports
DOI 10.1038/s41598-025-94437-9PMC PMC11929788
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Abstract

Medical image segmentation represents a pivotal and intricate procedure in the domain of medical image processing and analysis. With the progression of artificial intelligence in recent years, the utilization of deep learning techniques for medical image segmentation has witnessed escalating popularity. Nevertheless, the intricate nature of medical image poses challenges on the segmentation of diminutive targets is still in its early stages. Current networks encounter difficulties in addressing the segmentation of exceedingly small targets, especially when the number of training samples is limited. To overcome this constraint, we have implemented a proficient strategy to enhance lesion images containing small targets and constrained samples. We introduce a segmentation framework termed STS-Net, specifically designed for small target segmentation. This framework leverages the established capacity of convolutional neural networks to acquire effective image representations. The proposed STS-Net network adopts a ResNeXt50-32x4d architecture as the encoder, integrating attention mechanisms during the encoding phase to amplify the feature representation capabilities of the network. We evaluated the proposed network on four publicly available datasets. Experimental results underscore the superiority of our approach in the domain of medical image segmentation, particularly for small target segmentation. The codes are available at https://github.com/zlxokok/STSNet .

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  • Under-segmentation, failing to fully capture lesion boundaries, can result in pathological regions being missed and thus left untreated.

    “under-segmentation-failing to fully capture lesion boundaries-can result in pathological regions being missed and thus left untreated (Jalalifar et al. 2022; Zhao et al. 2025).”

    From Conformal Lesion Segmentation for 3D Medical Images (2025) · cites this paper as Zhao et al. 2025

    The Experimental results section reports under-segmented lesion pixels and partial missed detections, supporting the claim that segmentation can fail to capture pathological regions. It does not say that missed regions are left untreated or report treatment outcomes. No page number is available in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Zhao, L., Wang, T., Chen, Y., Zhang, X., Tang, H., Lin, F., Li, C., Li, Q., Tan, T., Kang, D., & Tong, T.
Affiliations
  • Fuzhou University (CN)
  • Fujian Medical University (CN)
  • Macao Polytechnic University (MO)
Corresponding author
  • Zhao, Longxuan
  • Kang, Dezhi
  • Tong, Tong
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-03-22
Volume
15
Issue
1
Pages
9924-9924
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-025-94437-9
PMC
PMC11929788
OpenAlex
W4408739104
Cited by
24
References
4
Citations by year
2026: 13 · 2025: 11
FWCI
8.43
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (62171133)
  • Fujian Health Commission, China (2022ZD01003)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
small object segmentation, medical image segmentation, attention mechanism, limited training data
MeSH terms
Deep Learning, Algorithms, Diagnostic Imaging, Humans, Image Processing, Computer-Assisted, Neural Networks, Computer

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