Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid

Thanh-Huy Nguyen, Thi Kim Ngan Ngo, Mai Anh Vu, Ting-Yuan Tu

CVPRW 2024 pp. 5223-5230

doi:10.1109/CVPRW63382.2024.00531 /cvprw/2024/nguyen2024cvprw-blurryconsistency/

Abstract

The ability of three-dimensional (3D) spheroid modeling to study the invasive behavior of breast cancer cells has drawn increased attention. The deep learning-based image processing framework is very effective at speeding up the cell morphological analysis process. Out-of-focus photos taken while capturing 3D cells under several z-slices, however, could negatively impact the deep learning model. In this work, we created a new algorithm to handle blurry images while preserving the stacked image quality. Furthermore, we proposed a unique training architecture that leverages consistency training to help reduce the bias of the model when dense-slice stacking is applied. Additionally, the model’s stability is increased under the sparse-slice stacking effect by utilizing the self-training approach. The new blurring stacking technique and training flow are combined with the suggested architecture and self-training mechanism to provide an innovative yet easy-to-use framework. Our methods produced noteworthy experimental outcomes in terms of both quantitative and qualitative aspects.

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Cite

Text

Nguyen et al. "Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00531

Markdown

[Nguyen et al. "Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/nguyen2024cvprw-blurryconsistency/) doi:10.1109/CVPRW63382.2024.00531

BibTeX

@inproceedings{nguyen2024cvprw-blurryconsistency,
  title     = {{Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid}},
  author    = {Nguyen, Thanh-Huy and Ngo, Thi Kim Ngan and Vu, Mai Anh and Tu, Ting-Yuan},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2024},
  pages     = {5223-5230},
  doi       = {10.1109/CVPRW63382.2024.00531},
  url       = {https://mlanthology.org/cvprw/2024/nguyen2024cvprw-blurryconsistency/}
}