论文标题
Multistar：用Star-Convex多边形对重叠对象的实例分割
MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons
论文作者
论文摘要
生物医学图像中重叠对象的实例分割仍然是一个未解决的问题。我们承担了这一挑战，并呈现了Multistar，这是流行实例分割方法Stardist的扩展。我们方法的主要新颖性是，我们确定对象重叠的像素并使用此信息来改进提案采样，并避免抑制真正重叠对象的建议。这使我们能够将Stardist的想法应用于具有重叠对象的图像，而与已建立的方法相比，仅产生一个小的开销。 Multistar在两个数据集上显示出令人鼓舞的结果，并具有使用简单易于训练的网络体系结构的优势。
Instance segmentation of overlapping objects in biomedical images remains a largely unsolved problem. We take up this challenge and present MultiStar, an extension to the popular instance segmentation method StarDist. The key novelty of our method is that we identify pixels at which objects overlap and use this information to improve proposal sampling and to avoid suppressing proposals of truly overlapping objects. This allows us to apply the ideas of StarDist to images with overlapping objects, while incurring only a small overhead compared to the established method. MultiStar shows promising results on two datasets and has the advantage of using a simple and easy to train network architecture.
