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

Abstract

The lack of standardized, objective tools for measuring biomarker morphology poses a significant obstacle to managing Microbial Keratitis (MK). Previous studies have demonstrated that robust segmentation benefits MK diagnosis, management, and estimation of visual outcomes. However, despite exciting advances, current methods cannot accurately detect biomarker boundaries and differentiate the overlapped regions in challenging cases. In this work, we propose a novel self-knowledge distillation-empowered directional connectivity transformer, called SDCTrans. We utilize the directional connectivity modeling framework to improve biomarker boundary detection. The transformer backbone and the hierarchical self-knowledge distillation scheme in this framework enhance directional representation learning. We also propose an efficient segmentation head design to effectively segment overlapping regions. This is the first work that successfully incorporates directional connectivity modeling with a transformer. SDCTrans trained and tested with a new large-scale MK dataset accurately and robustly segments crucial biomarkers in three types of slit lamp biomicroscopy images. Through comprehensive experiments, we demonstrated the superiority of the proposed SDCTrans over current state-of-the-art models. We also show that our SDCTrans matches, if not outperforms, the performance of expert human graders in MK biomarker identification and visual acuity outcome estimation. Experiments on skin lesion images are also included as an illustrative example of SDCTrans' utility in other segmentation tasks. The new MK dataset and codes are available at https://github.com/Zyun-Y/SDCTrans.

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