This work is motivated by the mostly unsolved task of parsing biological
images with multiple overlapping articulated model organisms (such as worms or
larvae). We present a general approach that separates the two main challenges
associated with such data, individual object shape estimation and object groups
disentangling. At the core of the approach is a deep feed-forward singling-out
network (SON) that is trained to map each local patch to a vectorial descriptor
that is sensitive to the characteristics (e.g. shape) of a central object,
while being invariant to the variability of all other surrounding elements.
Given a SON, a local image patch can be matched to a gallery of isolated
elements using their SON-descriptors, thus producing a hypothesis about the
shape of the central element in that patch. The image-level optimization based
on integer programming can then pick a subset of the hypotheses to explain
(parse) the whole image and disentangle groups of organisms.
While sharing many similarities with existing "analysis-by-synthesis"
approaches, our method avoids the need for stochastic search in the
high-dimensional configuration space and numerous rendering operations at
test-time. We show that our approach can parse microscopy images of three
popular model organisms (the C.Elegans roundworms, the Drosophila larvae, and
the E.Coli bacteria) even under significant crowding and overlaps between
organisms. We speculate that the overall approach is applicable to a wider
class of image parsing problems concerned with crowded articulated objects, for
which rendering training images is possible.

本文提出了一种深度前馈的分离网络（SON）方法，能够有效地分离高度重叠的生物图像中的个体对象，并估计中心对象的形状。实验表明该方法可以解析三类流行的模型生物的显微镜图像，同时避免了无需渲染训练图像的高维度配置空间和大量渲染测试时间操作的需求。