We present CLUSTSEG, a general, transformer-based framework that tackles
different image segmentation tasks (i.e., superpixel, semantic, instance, and
panoptic) through a unified neural clustering scheme. Regarding queries as
cluster centers, CLUSTSEG is innovative in two aspects:1) cluster centers are
initialized in heterogeneous ways so as to pointedly address task-specific
demands (e.g., instance- or category-level distinctiveness), yet without
modifying the architecture; and 2) pixel-cluster assignment, formalized in a
cross-attention fashion, is alternated with cluster center update, yet without
learning additional parameters. These innovations closely link CLUSTSEG to EM
clustering and make it a transparent and powerful framework that yields
superior results across the above segmentation tasks.

CLUSTSEG 是一个基于 transformer 的通用框架，通过统一的神经聚类方案解决不同的图像分割任务，并通过两个创新方面实现。像 EM 聚类一样，这些创新使 CLUSTSEG 成为一个透明而强大的框架，在上述分割任务中产生优越的结果。

CLUSTSEG: 通用分割的聚类

CLUSTSEG: Clustering for Universal Segmentation

Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT
(NCCT) scans is significant in clinical practice. Existing hemorrhage
segmentation methods usually ignores the anisotropic nature of the NCCT, and
are evaluated on different in-house datasets with distinct metrics, making it
highly challenging to improve segmentation performance and perform objective
comparisons among different methods. The INSTANCE 2022 was a grand challenge
held in conjunction with the 2022 International Conference on Medical Image
Computing and Computer Assisted Intervention (MICCAI). It is intended to
resolve the above-mentioned problems and promote the development of both
intracranial hemorrhage segmentation and anisotropic data processing. The
INSTANCE released a training set of 100 cases with ground-truth and a
validation set with 30 cases without ground-truth labels that were available to
the participants. A held-out testing set with 70 cases is utilized for the
final evaluation and ranking. The methods from different participants are
ranked based on four metrics, including Dice Similarity Coefficient (DSC),
Hausdorff Distance (HD), Relative Volume Difference (RVD) and Normalized
Surface Dice (NSD). A total of 13 teams submitted distinct solutions to resolve
the challenges, making several baseline models, pre-processing strategies and
anisotropic data processing techniques available to future researchers. The
winner method achieved an average DSC of 0.6925, demonstrating a significant
growth over our proposed baseline method. To the best of our knowledge, the
proposed INSTANCE challenge releases the first intracranial hemorrhage
segmentation benchmark, and is also the first challenge that intended to
resolve the anisotropic problem in 3D medical image segmentation, which
provides new alternatives in these research fields.

该论文介绍了一个名为 INSTANCE 的大型挑战赛，旨在为研究人员提供解决自动颅内出血分割问题和非均异性数据处理问题的基准测试和方法。INSTANCE 提供一套包含 100 个带有真实标签和 30 个没有真实标签的验证数据的训练库，以及用于最终评估的 70 个带有真实标签的测试数据库。13 个团队提交的解决方案中，其中一个方法的 DSC 平均值为 0.6925，相比我们提出的基准方法有了显著提升。

非对比头部 CT 上最先进的三维各向异性颅内出血分割：INSTANCE 挑战赛

The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation  on non-contrast head CT: The INSTANCE challenge

Reasoning over an instance composed of a set of vectors, like a point cloud,
requires that one accounts for intra-set dependent features among elements.
However, since such instances are unordered, the elements' features should
remain unchanged when the input's order is permuted. This property, permutation
equivariance, is a challenging constraint for most neural architectures. While
recent work has proposed global pooling and attention-based solutions, these
may be limited in the way that intradependencies are captured in practice. In
this work we propose a more general formulation to achieve permutation
equivariance through ordinary differential equations (ODE). Our proposed
module, Exchangeable Neural ODE (ExNODE), can be seamlessly applied for both
discriminative and generative tasks. We also extend set modeling in the
temporal dimension and propose a VAE based model for temporal set modeling.
Extensive experiments demonstrate the efficacy of our method over strong
baselines.

本文提出了一种基于常微分方程（ODE）的可交换神经网络模组（Exchangeable Neural ODE），使神经网络在处理无序矢量组时可以在满足排列等变性（permutation equivariance）的前提下捕捉各元素的内部依赖特征，并在时间维度上对集合建模，同时进行了大量实验证明其效果优于基线模型。