Instance segmentation of biological images is essential for studying object
behaviors and properties. The challenges, such as clustering, occlusion, and
adhesion problems of the objects, make instance segmentation a non-trivial
task. Current box-free instance segmentation methods typically rely on local
pixel-level information. Due to a lack of global object view, these methods are
prone to over- or under-segmentation. On the contrary, the box-based instance
segmentation methods incorporate object detection into the segmentation,
performing better in identifying the individual instances. In this paper, we
propose a new box-based instance segmentation method. Mainly, we locate the
object bounding boxes from their center points. The object features are
subsequently reused in the segmentation branch as a guide to separate the
clustered instances within an RoI patch. Along with the instance normalization,
the model is able to recover the target object distribution and suppress the
distribution of neighboring attached objects. Consequently, the proposed model
performs excellently in segmenting the clustered objects while retaining the
target object details. The proposed method achieves state-of-the-art
performances on three biological datasets: cell nuclei, plant phenotyping
dataset, and neural cells.

本文提出了一种基于盒子的实例分割方法，通过引入目标检测和实例规范化技术，实现在生物图像识别中更准确的实例分割。

生物图像的物体引导实例分割

Object-Guided Instance Segmentation for Biological Images

We present a new instance segmentation approach tailored to biological
images, where instances may correspond to individual cells, organisms or plant
parts. Unlike instance segmentation for user photographs or road scenes, in
biological data object instances may be particularly densely packed, the
appearance variation may be particularly low, the processing power may be
restricted, while, on the other hand, the variability of sizes of individual
instances may be limited. The proposed approach successfully addresses these
peculiarities.
Our approach describes each object instance using an expectation of a limited
number of sine waves with frequencies and phases adjusted to particular object
sizes and densities. At train time, a fully-convolutional network is learned to
predict the object embeddings at each pixel using a simple pixelwise regression
loss, while at test time the instances are recovered using clustering in the
embedding space. In the experiments, we show that our approach outperforms
previous embedding-based instance segmentation approaches on a number of
biological datasets, achieving state-of-the-art on a popular CVPPP benchmark.
This excellent performance is combined with computational efficiency that is
needed for deployment to domain specialists.
The source code of the approach is available at
this https URL

该论文提出了一种适用于生物学图像的新型实例分割方法，利用正弦波来描述每个对象实例，并利用聚类在嵌入空间中恢复实例，该方法在多个生物学数据集上表现出色且计算效率高。

使用谐嵌入进行生物图像的实例分割

Instance Segmentation of Biological Images Using Harmonic Embeddings

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。