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Self-knowledge distillation-empowered directional connectivity transformer for microbial keratitis biomarkers segmentation on slit-lamp images

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doi: 10.1016/j.media.2025.103533.


Epub 2025 Mar 13.

Affiliations

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Ziyun Yang et al.


Med Image Anal.


2025 May.

Abstract

The lack of standardized, goal instruments for measuring biomarker morphology poses a big impediment to managing Microbial Keratitis (MK). Previous research have demonstrated that strong segmentation advantages MK analysis, administration, and estimation of visible outcomes. However, regardless of thrilling advances, present strategies can’t precisely detect biomarker boundaries and differentiate the overlapped areas in difficult instances. In this work, we suggest a novel self-knowledge distillation-empowered directional connectivity transformer, referred to as SDCTrans. We make the most of the directional connectivity modeling framework to enhance biomarker boundary detection. The transformer spine and the hierarchical self-knowledge distillation scheme on this framework improve directional illustration studying. We additionally suggest an environment friendly segmentation head design to successfully phase overlapping areas. This is the primary work that efficiently incorporates directional connectivity modeling with a transformer. SDCTrans skilled and examined with a brand new large-scale MK dataset precisely and robustly segments essential biomarkers in three sorts of slit lamp biomicroscopy photos. Through complete experiments, we demonstrated the prevalence of the proposed SDCTrans over present state-of-the-art fashions. We additionally present that our SDCTrans matches, if not outperforms, the efficiency of knowledgeable human graders in MK biomarker identification and visible acuity final result estimation. Experiments on pores and skin lesion photos are additionally included as an illustrative instance of SDCTrans’ utility in different segmentation duties. The new MK dataset and codes can be found at


Keywords:

Connectivity modeling; Medical segmentation; Microbial keratitis; Ocular segmentation; Self-knowledge distillation; Transformer.

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Conflict of curiosity assertion

Declaration of competing curiosity The authors declare that they haven’t any identified competing monetary pursuits or private relationships that might have appeared to affect the work reported on this paper. Declaration of competing curiosity The authors declare that they haven’t any identified competing monetary pursuits or private relationships that might have appeared to affect the work reported on this paper.

Figures


Figure 1:



Figure 1:

Example of MK biomarkers in white gentle Slit-lamp picture. The activity is difficult as a consequence of closely overlapping between biomarkers, lack of particular borders, and knowledge imbalance.


Figure 2:



Figure 2:

Comparison of various modeling strategies. Connectivity modeling (b)-(d) displays benefits over basic modeling (a) e.g., (Ronneberger et al., 2015). In (b), (Yang et al., 2022b) the latent house of the connectivity community is undefined. In (c) (Yang and Farsiu, 2023), directional options had been extracted within the bottleneck and handed to the decoder with out regularization. Frequent interactions between the 2 branches result in the fusion of directional and categorical options. In (d), our mannequin provides further regularization to the decoder to make sure the consistency of the directional embeddings.


Figure 3:



Figure 3:

Overview of the SDCTrans structure, together with a Transformer-based encoder and sub-path directional excitation (SDE) module, a Hierarchical Self-knowledge Distillated Decoder (HSDD), and a number of other Class-specific Connectivity-based Segmentation heads (CCSHs).


Figure 4:



Figure 4:

The ith and (i+1)th Space Blocks and the DSKD module between them. The directional embedding was distilled with cosine similarity from one stage to the subsequent.


Figure 5:



Figure 5:

CCSH module. For every biomarker, multi-scale function maps shall be built-in into the connectivity map earlier than the ultimate prediction. CBR stands for Convolution, Batch-Norm, and ReLu.


Figure 6:



Figure 6:

Visualization of the segmentation efficiency of SDCTrans and different best-performed fashions in every class: CE-Net and DconnNet (CNN-based), H2Former (transformer-based), and SLIT-Net (MK) in white gentle, blue gentle, and ScS SLP photos.

References

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