The automatic segmentation of blood vessels in fundus images can help analyze the condition of retinal vasculature, which is crucial for identifying various systemic diseases like hypertension, diabetes, etc. Despite the success of Deep Learning-based models in this segmentation task, most of them are heavily parametrized and thus have limited use in practical applications. This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model that requires significantly fewer parameters and yet delivers performance similar to existing models. The model makes use of the excellent segmentation capabilities of Iternet architecture but overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it. Thus, the new model reduces parameters without any compromise with the network's depth, which is necessary to learn abstract hierarchical concepts in deep models. This lightweight segmentation model speeds up training and inference time and is potentially helpful in the medical domain where data is scarce and, therefore, heavily parametrized models tend to overfit. The proposed model was evaluated on three publicly available datasets: DRIVE, STARE, and CHASE-DB1. Further cross-training and inter-rater variability evaluations have also been performed. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases.
翻译:在Funtus图像中自动分解血管血管可以帮助分析视网膜血管病的状况,这是确定高血压、糖尿病等各种系统疾病的关键。 尽管深学习模型在这一分解任务中取得了成功,但大多数以血浆为基础的模型都严重不对称,因此在实际应用方面用处有限。本文建议使用ItermiUnet,这是一个新的轻量分解模型,它要求大大降低参数,但能提供与现有模型相似的性能。模型利用了Iternet结构的极好的分解能力,但克服了它高度的分解性质。因此,新模型在不与网络深度发生任何妥协的情况下降低了参数,而这是在深层模型中学习抽象的等级概念所必要的。这种轻量分解模型加快了培训和推导时间,在数据稀少、因此严重分解模型往往过分适合的医学领域可能有所帮助。在三种公开的数据集上对拟议模型进行了评估:Drevive、STARE和CHASEDB进行早期分析,还利用了多种变异性和跨工具。