DiffusionDCI: A Novel Diffusion-Based Unified Framework for Dynamic Full-Field OCT Image Generation and Segmentation

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dc.citation.volume12
dc.contributor.authorYang B
dc.contributor.authorLi J
dc.contributor.authorWang J
dc.contributor.authorLi R
dc.contributor.authorGu K
dc.contributor.authorLiu B
dc.contributor.editorMilitello C
dc.date.accessioned2024-10-01T20:43:31Z
dc.date.available2024-10-01T20:43:31Z
dc.date.issued2024
dc.description.abstractRapid and accurate identification of cancerous areas during surgery is crucial for guiding surgical procedures and reducing postoperative recurrence rates. Dynamic Cell Imaging (DCI) has emerged as a promising alternative to traditional frozen section pathology, offering high-resolution displays of tissue structures and cellular characteristics. However, challenges persist in segmenting DCI images using deep learning methods, such as color variation and artifacts between patches in whole slide DCI images, and the difficulty in obtaining precise annotated data. In this paper, we introduce a novel two-stage framework for DCI image generation and segmentation. Initially, the Dual Semantic Diffusion Model (DSDM) is specifically designed to generate high-quality and semantically relevant DCI images. These images not only serve as an effective means of data augmentation to assist downstream segmentation tasks but also help in reducing the reliance on expensive and hard-to-obtain large annotated medical image datasets. Furthermore, we reuse the pretrained DSDM to extract diffusion features, which are then infused into the segmentation network via a cross-attention alignment module. This approach enables our network to capture and utilize the characteristics of DCI images more effectively, thereby significantly enhancing segmentation results. Our method was validated on the DCI dataset and compared with other methods for image generation and segmentation. Experimental results demonstrate that our method achieves superior performance in both tasks, proving the effectiveness of the proposed model.
dc.description.confidentialfalse
dc.edition.edition2024
dc.format.pagination37702-37714
dc.identifier.citationYang B, Li J, Wang J, Li R, Gu K, Liu B. (2024). DiffusionDCI: A Novel Diffusion-Based Unified Framework for Dynamic Full-Field OCT Image Generation and Segmentation. IEEE Access. 12. (pp. 37702-37714).
dc.identifier.doi10.1109/ACCESS.2024.3372863
dc.identifier.eissn2169-3536
dc.identifier.elements-typejournal-article
dc.identifier.number3372863
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71546
dc.languageEnglish
dc.publisherIEEE Access
dc.publisher.urihttps://ieeexplore.ieee.org/document/10458118
dc.relation.isPartOfIEEE Access
dc.rights(c) 2024 The Author/s
dc.rightsCC BY-NC-ND 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSemantic diffusion model
dc.subjectimage synthesis
dc.subjectimage segmentation
dc.subjectdynamic cell imaging
dc.titleDiffusionDCI: A Novel Diffusion-Based Unified Framework for Dynamic Full-Field OCT Image Generation and Segmentation
dc.typeJournal article
pubs.elements-id487292
pubs.organisational-groupOther
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