paper_authors: Pavel Sinha, Ioannis Psaromiligkos, Zeljko Zilic
For: automatic segmentation of lumen and media in IntraVascular ultra-sound (IVUS) images* Methods: closed polygonal chains, adaptive-subband-decomposition CNN, Jaccard Measure (JM) loss function, Mean Squared Error (MSE) loss function* Results: outperforms state-of-the-art lumen and media segmentation methods, using JM and Hausdorff Distance (HD) metrics.Here’s the full text in Simplified Chinese:* 为: 自动分割IVUS图像中的血液管和媒质区域* 方法: 使用闭合多边形链、自适应分割Deep Learning网络、Jaccard度量函数和 Mean Squared Error loss function* 结果: 与状态艺术精度segmentation方法相比，达到了更高的精度，使用JM和 HausdorffDistance 度量函数。Abstract
We propose an automatic segmentation method for lumen and media with irregular contours in IntraVascular ultra-sound (IVUS) images. In contrast to most approaches that broadly label each pixel as either lumen, media, or background, we propose to approximate the lumen and media contours by closed polygonal chains. The chain vertices are placed at fixed angles obtained by dividing the entire 360\degree~angular space into equally spaced angles, and we predict their radius using an adaptive-subband-decomposition CNN. We consider two loss functions during training. The first is a novel loss function using the Jaccard Measure (JM) to quantify the similarities between the predicted lumen and media segments and the corresponding ground-truth image segments. The second loss function is the traditional Mean Squared Error. The proposed architecture significantly reduces computational costs by replacing the popular auto-encoder structure with a simple CNN as the encoder and the decoder is reduced to simply joining the consecutive predicted points. We evaluated our network on the publicly available IVUS-Challenge-2011 dataset using two performance metrics, namely JM and Hausdorff Distance (HD). The evaluation results show that our proposed network mostly outperforms the state-of-the-art lumen and media segmentation methods.
摘要
Translated into Simplified Chinese:我们提出了一种自动分割方法，用于在IntraVascular ultra-sound（IVUS）图像中分割不规则的液腔和媒腔。与大多数方法不同，我们使用closed polygon链来近似液腔和媒腔的边界。链Vertex是在分割360度的angular space的等间隔angles中得到的，并使用适应性子带分解CNN来预测它们的半径。我们在训练时使用了两种损失函数：一种新的损失函数使用Jaccard Measure（JM）来衡量预测的液腔和媒腔段与对应的真实图像段的相似性，以及传统的Mean Squared Error。我们提出的 Architecture significantly reduces computational costs by replacing the popular auto-encoder structure with a simple CNN as the encoder and the decoder is reduced to simply joining the consecutive predicted points。我们在公共可用的IVUS-Challenge-2011 dataset上评估了我们的网络，使用了两个性能指标：JM和Hausdorff Distance（HD）。评估结果显示，我们的提出的网络大多比现状的液腔和媒腔分割方法表现更好。
Prescanning Assembly Optimization Criteria for Computed Tomography
results: 提高样本位置的优化可以减少50%的平均平方根误差，16.5%的相似性指标下降和40%的抖动噪声在重建图像中，并自动化计算机断层扫描Assembly，从而节省时间、剂量和运行成本。Abstract
Computerized Tomography assembly and system configuration are optimized for enhanced invertibility in sparse data reconstruction. Assembly generating maximum principal components/condition number of weight matrix is designated as best configuration. The gamma CT system is used for testing. The unoptimized sample location placement with 7.7% variation results in a maximum 50% root mean square error, 16.5% loss of similarity index, and 40% scattering noise in the reconstructed image relative to the optimized sample location when the proposed criteria are used. The method can help to automate the CT assembly, resulting in relatively artifact-free recovery and reducing the iteration to figure out the best scanning configuration for a given sample size, thus saving time, dosage, and operational cost.
摘要
Translated into Simplified Chinese:计算机Tomography组装和系统配置被优化以提高逆向性能。生成最大主要components/condition number的weight矩阵 assembly被认为是最佳配置。使用gamma CT系统进行测试。未优化的样本位置布局 resulted in 7.7%变化，导致最大50%根圆振幅误差，16.5%相似性指标损失，和40%扩散噪声在重构图像中相对于优化样本位置when使用提议的标准。这种方法可以帮助自动化CT组装，从而获得相对 artifact-free 的恢复，并降低确定给定样本大小的扫描配置的迭代次数，从而节省时间、剂量和操作成本。
FreqAlign: Excavating Perception-oriented Transferability for Blind Image Quality Assessment from A Frequency Perspective
results: 提出了一种有效的频谱对齐策略（FreqAlign），通过研究不同频谱 комponents的可见性，选择最适合性的频谱component进行对齐，从而提高BIQA的转移性能。Abstract
Blind Image Quality Assessment (BIQA) is susceptible to poor transferability when the distribution shift occurs, e.g., from synthesis degradation to authentic degradation. To mitigate this, some studies have attempted to design unsupervised domain adaptation (UDA) based schemes for BIQA, which intends to eliminate the domain shift through adversarial-based feature alignment. However, the feature alignment is usually taken at the low-frequency space of features since the global average pooling operation. This ignores the transferable perception knowledge in other frequency components and causes the sub-optimal solution for the UDA of BIQA. To overcome this, from a novel frequency perspective, we propose an effective alignment strategy, i.e., Frequency Alignment (dubbed FreqAlign), to excavate the perception-oriented transferability of BIQA in the frequency space. Concretely, we study what frequency components of features are more proper for perception-oriented alignment. Based on this, we propose to improve the perception-oriented transferability of BIQA by performing feature frequency decomposition and selecting the frequency components that contained the most transferable perception knowledge for alignment. To achieve a stable and effective frequency selection, we further propose the frequency movement with a sliding window to find the optimal frequencies for alignment, which is composed of three strategies, i.e., warm up with pre-training, frequency movement-based selection, and perturbation-based finetuning. Extensive experiments under different domain adaptation settings of BIQA have validated the effectiveness of our proposed method. The code will be released at https://github.com/lixinustc/Openworld-IQA.
摘要
《盲目图像质量评估（BIQA）容易受到分布转移的影响，例如从生成过程破坏到真实破坏。为了解决这个问题，一些研究已经尝试了基于无监督领域适应（UDA）的BIQA方法，以减少领域的转移。然而，通常情况下，这些方法会在低频空间的特征上进行对应性对齐，这会忽略其他频率组件中的可以传递的感知知识，导致UDA的BIQA方法不优。为了解决这个问题，我们从一种新的频率视角出发，提出了一种有效的对齐策略——频率对齐（dubbed FreqAlign），以挖掘BIQA中的感知导向的传递性。》具体来说，我们研究了哪些特征频率是更适合用于感知导向的对齐。基于这个，我们提出了改进BIQA的感知导向传递性的方法，通过特征频率分解和选择包含最多可传递的感知知识的频率组件进行对齐。为了实现稳定和有效的频率选择，我们还提出了频率移动的滑块窗口来找到最佳对齐频率，这包括三种策略：启动预训练、频率移动选择和扰动训练。我们的方法在BIQA的不同领域适应设置下进行了广泛的实验，并证明了我们提出的方法的效果。BIQA代码将在 GitHub 上发布。