The task of blood vessel segmentation in microscopy images is crucial for many diagnostic and research applications. However, vessels can look vastly different, depending on the transient imaging conditions, and collecting data for supervised training is laborious. We present a novel deep learning method for unsupervised segmentation of blood vessels. The method is inspired by the field of active contours and we introduce a new loss term, which is based on the morphological Active Contours Without Edges (ACWE) optimization method. The role of the morphological operators is played by novel pooling layers that are incorporated to the network's architecture.We demonstrate the challenges that are faced by previous supervised learning solutions, when the imaging conditions shift. Our unsupervised method is able to outperform such previous methods in both the labeled dataset, and when applied to similar but different datasets. Our code, as well as efficient PyTorch reimplementations of the baseline methods VesselNN and DeepVess is available on GitHub - https://github.com/shirgur/UMIS.

使用深度學習的方法，在显微镜图像中实现无监督的血管分割。通过引入一种基于形态学活动轮廓的未标注数据的新损失函数，利用包含于网络体系结构中的新型池化层，提出的无监督方法能够胜过之前的有监督的学习方法，并且在类似但不同的数据集上也能表现出更好的结果。

一种仿神经元活跃轮廓方法进行无监督的微细血管图像分割