Progressive Distillation for Incremental Learning in Corneal Confocal Microscopy Segmentation.

Abstract

The morphological changes of corneal structures captured by corneal confocal microscopy (CCM), such as corneal nerves, Langerhans cells, stromal cells, etc., are closely related to various ocular and systemic diseases. Current CCM segmentation methods primarily focus on single-task, which limits their broad applicability in clinical practice. The absence of a standardized benchmark further presents a significant challenge in evaluating new methods. To this end, this paper presents a novel incremental learning-based approach for multi-structure segmentation in CCM images and a new benchmark. Specifically, we first propose a data fingerprint distillation (FIND) module to encode task-relevant knowledge by extracting compact representations of structures from CCM images via structural importance mapping. Building on FIND, we propose a progressive task-guided adapter learning (ProTA) strategy, which refines the model's representation of structures through a series of "easy-to-hard" distillation stages. ProTA dynamically adjusts the scope of task-relevant knowledge extracted by FIND, thereby improving the model's ability to accurately discriminate between multiple structures while enhancing knowledge transfer efficiency. Extensive experiments demonstrate that the proposed method achieves the state-of-the-art performance in terms of all corneal structures segmentation. We also demonstrate our approach's plug-and-play capability across four other medical image modalities, suggesting its potential as a general incremental learning tool. Additionally, this work seeks to provide a benchmark tool comprising a comprehensive dataset and their fine manual annotation, as well as unified benchmarking evaluations for state-of-the-art methods. All the dataset, source code and evaluation tool are publicly available at https://github.com/iMED-Lab/CCM-Pro.