Deep-learning-based segmentation methods have shown great success across many medical image applications. However, the custom training paradigms suffer from a well-known constraint of the requirement of pixel-wise annotations, which is labor-intensive, especially when they are required to learn new classes incrementally. Contemporary incremental learning focuses on dealing with catastrophic forgetting in image classification and object detection. However, this work aims to promote the performance of the current model to learn new classes with the help of the previous model in the context of incremental learning of instance segmentation. It enormously benefits the current model when the labeled data is limited because of the high labor intensity of manual labeling. In this paper, on the Diabetic Retinopathy (DR) lesion segmentation problem, a novel incremental segmentation paradigm is proposed to distill the knowledge of the previous model to improve the current model. Remarkably, we propose various approaches working on the class-based alignment of the probability maps of the current and the previous model, accounting for the difference between the background classes of the two models. The experimental evaluation of DR lesion segmentation shows the effectiveness of the proposed approaches.

基于深度学习的分割方法已经在许多医学图像应用中取得了巨大的成功。但是，自定义训练范式受到众所周知的像素级注释要求的约束，这是劳动密集型的，特别是当需要它们逐步学习新的类时。当代的增量学习专注于处理图像分类和对象检测中的灾难性遗忘。但是，这项工作的目的是在实例分割的增量学习的背景下，借助以前的模型来提高当前模型学习新类的性能。当由于手工标注的劳动强度高而使标注的数据受到限制时，它将极大地有益于当前模型。本文针对糖尿病视网膜病变（DR）病变分割问题，提出了一种新颖的增量分割范例，以提炼先前模型的知识，以改进当前模型。值得注意的是，我们提出了各种方法来解决当前模型和先前模型的概率图的基于类的比对问题，并考虑了两个模型的背景类之间的差异。DR病灶分割的实验评估表明了所提出方法的有效性。