We present a novel method for proposal free instance segmentation that can
handle sophisticated object shapes which span large parts of an image and form
dense object clusters with crossovers. Our method is based on predicting dense
local shape descriptors, which we assemble to form instances. All instances are
assembled simultaneously in one go. To our knowledge, our method is the first
non-iterative method that yields instances that are composed of learnt shape
patches. We evaluate our method on a diverse range of data domains, where it
defines the new state of the art on four benchmarks, namely the ISBI 2012 EM
segmentation benchmark, the BBBC010 C. elegans dataset, and 2d as well as 3d
fluorescence microscopy data of cell nuclei. We show furthermore that our
method also applies to 3d light microscopy data of Drosophila neurons, which
exhibit extreme cases of complex shape clusters

本研究提出了一种新颖的无需提案的实例分割方法，其可处理跨越图像大部分并形成密集对象集群的复杂对象形状。该方法基于预测密集局部形状描述符，将其组装成实例。我们的方法是首个生成由学习的形状补丁组成的实例的非迭代方法，并在多个数据领域上进行了评估，其中定义了四个基准，分别是 ISBI 2012 EM 分割基准，BBBC010 C.elegans 数据集，以及二维和三维荧光显微镜图像的细胞核。此外，我们还展示了我们的方法也适用于展示了复杂形状集群极端案例的果蝇神经元的三维光学显微镜数据。