Accurately segmenting the pancreas remains a huge challenge. Traditional
methods encounter difficulties in semantic localization due to the small volume
and distorted structure of the pancreas, while deep learning methods encounter
challenges in obtaining accurate edges because of low contrast and organ
overlapping. To overcome these issues, we propose a multi-cue level set method
based on the diffusion probabilistic model, namely Diff-mcs. Our method adopts
a coarse-to-fine segmentation strategy. We use the diffusion probabilistic
model in the coarse segmentation stage, with the obtained probability
distribution serving as both the initial localization and prior cues for the
level set method. In the fine segmentation stage, we combine the prior cues
with grayscale cues and texture cues to refine the edge by maximizing the
difference between probability distributions of the cues inside and outside the
level set curve. The method is validated on three public datasets and achieves
state-of-the-art performance, which can obtain more accurate segmentation
results with lower uncertainty segmentation edges. In addition, we conduct
ablation studies and uncertainty analysis to verify that the diffusion
probability model provides a more appropriate initialization for the level set
method. Furthermore, when combined with multiple cues, the level set method can
better obtain edges and improve the overall accuracy. Our code is available at
this https URL

准确地划分胰腺仍然是一个巨大的挑战。为了克服传统方法和深度学习方法在语义定位和边缘精确性方面的困难，我们提出了一种基于扩散概率模型的多线索水平集方法，命名为 Diff-mcs。通过在粗分割阶段中应用扩散概率模型，我们使用获得的概率分布作为初始定位和先验线索，在精细分割阶段中，我们结合灰度线索和纹理线索以通过最大化线索内外的概率分布差异来改进边缘。该方法在三个公共数据集上验证并取得了最先进的性能，能够获得更准确的分割结果和较低不确定性的分割边缘。此外，我们还进行了剥离研究和不确定性分析，验证了扩散概率模型为水平集方法提供了更合适的初始化。此外，当与多个线索结合时，水平集方法可以更好地获得边缘并提高整体准确性。

扩散概率多线索水平集用于减少胰腺分割中的边缘不确定性

Diffusion Probabilistic Multi-cue Level Set for Reducing Edge  Uncertainty in Pancreas Segmentation

Amodal object segmentation is a challenging task that involves segmenting
both visible and occluded parts of an object. In this paper, we propose a novel
approach, called Coarse-to-Fine Segmentation (C2F-Seg), that addresses this
problem by progressively modeling the amodal segmentation. C2F-Seg initially
reduces the learning space from the pixel-level image space to the
vector-quantized latent space. This enables us to better handle long-range
dependencies and learn a coarse-grained amodal segment from visual features and
visible segments. However, this latent space lacks detailed information about
the object, which makes it difficult to provide a precise segmentation
directly. To address this issue, we propose a convolution refine module to
inject fine-grained information and provide a more precise amodal object
segmentation based on visual features and coarse-predicted segmentation. To
help the studies of amodal object segmentation, we create a synthetic amodal
dataset, named as MOViD-Amodal (MOViD-A), which can be used for both image and
video amodal object segmentation. We extensively evaluate our model on two
benchmark datasets: KINS and COCO-A. Our empirical results demonstrate the
superiority of C2F-Seg. Moreover, we exhibit the potential of our approach for
video amodal object segmentation tasks on FISHBOWL and our proposed MOViD-A.
Project page at: this http URL

通过逐步建模视觉特征和粗略预测分割的精细信息，我们提出了一种名为 “Coarse-to-Fine Segmentation (C2F-Seg)” 的新方法来解决涉及对象可见和遮挡部分的不完全分割的挑战。同时，我们创建了一个合成的整体性数据集 MOViD-A，用于图像和视频整体性对象分割。通过在两个基准数据集 KINS 和 COCO-A 上进行大量评估，我们实证了 C2F-Seg 的优越性，并展示了在 FISHBOWL 和我们提出的 MOViD-A 上进行视频整体性对象分割任务的潜力。

基于形状先验的粗细阶段分离

Coarse-to-Fine Amodal Segmentation with Shape Prior

Few-shot semantic segmentation aims at learning to segment a target object
from a query image using only a few annotated support images of the target
class. This challenging task requires to understand diverse levels of visual
cues and analyze fine-grained correspondence relations between the query and
the support images. To address the problem, we propose Hypercorrelation Squeeze
Networks (HSNet) that leverages multi-level feature correlation and efficient
4D convolutions. It extracts diverse features from different levels of
intermediate convolutional layers and constructs a collection of 4D correlation
tensors, i.e., hypercorrelations. Using efficient center-pivot 4D convolutions
in a pyramidal architecture, the method gradually squeezes high-level semantic
and low-level geometric cues of the hypercorrelation into precise segmentation
masks in coarse-to-fine manner. The significant performance improvements on
standard few-shot segmentation benchmarks of PASCAL-5i, COCO-20i, and FSS-1000
verify the efficacy of the proposed method.

本文提出了利用多层特征相关性和高效 4D 卷积的 HSNet 来解决少样本语义分割问题，通过 coarse-to-fine 方式逐渐将高层次和低层次的几何和语义信息聚合到精确的分割掩模中，并在 PASCAL-5i、COCO-20i 和 FSS-1000 标准基准上验证了该方法的有效性。