Click-point-based interactive segmentation has received widespread attention
due to its efficiency. However, it's hard for existing algorithms to obtain
precise and robust responses after multiple clicks. In this case, the
segmentation results tend to have little change or are even worse than before.
To improve the robustness of the response, we propose a structured click intent
model based on graph neural networks, which adaptively obtains graph nodes via
the global similarity of user-clicked Transformer tokens. Then the graph nodes
will be aggregated to obtain structured interaction features. Finally, the dual
cross-attention will be used to inject structured interaction features into
vision Transformer features, thereby enhancing the control of clicks over
segmentation results. Extensive experiments demonstrated the proposed algorithm
can serve as a general structure in improving Transformer-based interactive
segmenta?tion performance. The code and data will be released at
this https URL

我们提出了一种基于图神经网络的结构点击意图模型，通过全局相似性获得用户点击的 Transformer token，然后聚合图节点以获得结构交互特征，最后使用双重交互注意力将结构交互特征注入视觉 Transformer 特征，从而增强点击对分割结果的控制，实验证明该算法能够提高基于 Transformer 的交互分割性能。

基于 Transformer 的交互式分割中的结构化点击控制

Structured Click Control in Transformer-based Interactive Segmentation

For click-based interactive segmentation methods, reducing the number of
clicks required to obtain a desired segmentation result is essential. Although
recent click-based methods yield decent segmentation results, we observe that
substantial amount of clicks are required to segment elongated regions. To
reduce the amount of user-effort required, we propose using lines instead of
clicks for such cases. In this paper, an interactive segmentation algorithm
which adaptively adopts either clicks or lines as input is proposed.
Experimental results demonstrate that using lines can generate better
segmentation results than clicks for several cases.

使用线条代替点击作为输入的交互式分割算法可减少用户所需的工作量，并能在多种情况下产生更好的分割结果。

Clicks2Line: 使用线条进行交互式图像分割

Clicks2Line: Using Lines for Interactive Image Segmentation

Stroke segmentation plays a crucial role in the diagnosis and treatment of
stroke patients by providing spatial information about affected brain regions
and the extent of damage. Segmenting stroke lesions accurately is a challenging
task, given that conventional manual techniques are time consuming and prone to
errors. Recently, advanced deep models have been introduced for general medical
image segmentation, demonstrating promising results that surpass many state of
the art networks when evaluated on specific datasets. With the advent of the
vision Transformers, several models have been introduced based on them, while
others have aimed to design better modules based on traditional convolutional
layers to extract long-range dependencies like Transformers. The question of
whether such high-level designs are necessary for all segmentation cases to
achieve the best results remains unanswered. In this study, we selected four
types of deep models that were recently proposed and evaluated their
performance for stroke segmentation: a pure Transformer-based architecture
(DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention
mechanisms in their design, an advanced hybrid model that incorporates CNNs
with Transformers (FCT), and the well- known self-adaptive nnUNet framework
with its configuration based on given data. We examined their performance on
two publicly available datasets, and found that the nnUNet achieved the best
results with the simplest design among all. Revealing the robustness issue of
Transformers to such variabilities serves as a potential reason for their
weaker performance. Furthermore, nnUNet's success underscores the significant
impact of preprocessing and postprocessing techniques in enhancing segmentation
results, surpassing the focus solely on architectural designs

本研究选择了四种深度模型进行中风分割，即纯 Transformer 结构 (DAE-Former)，两种带有注意机制的先进 CNN 模型 (LKA 和 DLKA)，一种将 CNN 与 Transformer 结合的高级混合模型 (FCT)，以及以给定数据为基础的自适应 nnUNet 框架，并在两个公开可用的数据集上进行了评估，结果表明 nnUNet 在所有模型中具有最简单的设计，并通过强调预处理和后处理技术来提高分割结果，超越了仅关注架构设计的重要性。

基于深度学习模型的脑卒中分割：一项比较研究

Brain Stroke Segmentation Using Deep Learning Models: A Comparative  Study

Semantic segmentation is an important preliminary step towards automatic
medical image interpretation. Recently deep convolutional neural networks have
become the first choice for the task of pixel-wise class prediction. While
incorporating prior knowledge about the structure of target objects has proven
effective in traditional energy-based segmentation approaches, there has not
been a clear way for encoding prior knowledge into deep learning frameworks. In
this work, we propose a new loss term that encodes the star shape prior into
the loss function of an end-to-end trainable fully convolutional network (FCN)
framework. We penalize non-star shape segments in FCN prediction maps to
guarantee a global structure in segmentation results. Our experiments
demonstrate the advantage of regularizing FCN parameters by the star shape
prior and our results on the ISBI 2017 skin segmentation challenge data set
achieve the first rank in the segmentation task among $21$ participating teams.

本文研究了使用卷积神经网络进行语义分割的方法，提出一种新的 loss term，将先验知识嵌入完全卷积网络中，通过对分割结果进行全局约束，使得分割效果更好。实验结果表明，使用 star shape prior 进行正则化的 FCN 模型在 ISBI 2017 皮肤分割任务中取得了最好的结果。