paper_authors: Nina I. Shamsi, Alex S. Xu, Lars A. Gjesteby, Laura J. Brattain for: 这种研究是为了提高自动轴迹追踪的效率和准确性，特别是在使用自动注释工具时。methods: 该研究使用了完全监督学习，以及特别是针对三维脑影像的分割和中轴检测技术。它们还使用了保持 topology 的方法，以确保分割后的组件保持几何连接性。results: 该研究提出了一种自动 morphological smoothing 技术，并通过使用几何评估来避免过细化。该技术可以提高分割和中轴检测评价指标，并且可以提高 Betti 错误率。它还可以保持脑影像中的几何连接性。Abstract
Automated axon tracing via fully supervised learning requires large amounts of 3D brain imagery, which is time consuming and laborious to obtain. It also requires expertise. Thus, there is a need for more efficient segmentation and centerline detection techniques to use in conjunction with automated annotation tools. Topology-preserving methods ensure that segmented components maintain geometric connectivity, which is especially meaningful for applications where volumetric data is used, and these methods often make use of morphological thinning algorithms as the thinned outputs can be useful for both segmentation and centerline detection of curvilinear structures. Current morphological thinning approaches used in conjunction with topology-preserving methods are prone to over-thinning and require manual configuration of hyperparameters. We propose an automated approach for morphological smoothing using geometric assessment of the radius of tubular structures in brain microscopy volumes, and apply average pooling to prevent over-thinning. We use this approach to formulate a loss function, which we call Geo-metric Assessment-driven Topological Smoothing loss, or GATS. Our approach increased segmentation and center-line detection evaluation metrics by 2%-5% across multiple datasets, and improved the Betti error rates by 9%. Our ablation study showed that geometric assessment of tubular structures achieved higher segmentation and centerline detection scores, and using average pooling for morphological smoothing in place of thinning algorithms reduced the Betti errors. We observed increased topological preservation during automated annotation of 3D axons volumes from models trained with GATS.
摘要
自动化轴索追踪通过完全监督学习需要大量的3D脑图像，这需要较长的时间和劳动力来获得，同时还需要专家知识。因此，有更高效的分割和中心线检测技术的需求，以便与自动注释工具结合使用。保持 topology 的方法可以确保分割的组件保持几何连接，特别是在使用 volume 数据时，这些方法通常使用 morphological thinning 算法，因为压缩输出可以用于分割和中心线检测柔软结构。现有的 morphological thinning 方法在与保持 topology 方法结合使用时容易过于细化，需要手动配置超参数。我们提出一种自动化的 morphological smoothing 方法，使用脑微scopy volume 中管道结构的半径的几何评估，并应用平均聚合来避免过度细化。我们称之为 Geo-metric Assessment-driven Topological Smoothing loss，或 GATS。我们的方法在多个数据集上提高了分割和中心线检测评价指标，提高了 Betti 错误率，并且在分割和中心线检测方面实现了更高的分割和中心线检测得分。我们的抽象研究表明，半径的几何评估可以提高分割和中心线检测得分，而使用平均聚合而不是压缩算法可以降低 Betti 错误。我们发现在使用 GATS 进行自动注释的3D轴索Volume中， topological 保持得到了改进。
Toward ground-truth optical coherence tomography via three-dimensional unsupervised deep learning processing and data
results: 实现高质量噪声自由3D成像，超过先前状态之最Abstract
Optical coherence tomography (OCT) can perform non-invasive high-resolution three-dimensional (3D) imaging and has been widely used in biomedical fields, while it is inevitably affected by coherence speckle noise which degrades OCT imaging performance and restricts its applications. Here we present a novel speckle-free OCT imaging strategy, named toward-ground-truth OCT (tGT-OCT), that utilizes unsupervised 3D deep-learning processing and leverages OCT 3D imaging features to achieve speckle-free OCT imaging. Specifically, our proposed tGT-OCT utilizes an unsupervised 3D-convolution deep-learning network trained using random 3D volumetric data to distinguish and separate speckle from real structures in 3D imaging volumetric space; moreover, tGT-OCT effectively further reduces speckle noise and reveals structures that would otherwise be obscured by speckle noise while preserving spatial resolution. Results derived from different samples demonstrated the high-quality speckle-free 3D imaging performance of tGT-OCT and its advancement beyond the previous state-of-the-art.
摘要
A Fast Algorithm for Low Rank + Sparse column-wise Compressive Sensing
results: 通过对一系列 simulations 进行详细的数值评估， authors 证明了 AltGDmin-LR+S 的性能。Abstract
This paper focuses studies the following low rank + sparse (LR+S) column-wise compressive sensing problem. We aim to recover an $n \times q$ matrix, $\X^* =[ \x_1^*, \x_2^*, \cdots , \x_q^*]$ from $m$ independent linear projections of each of its $q$ columns, given by $\y_k :=\A_k\x_k^*$, $k \in [q]$. Here, $\y_k$ is an $m$-length vector with $m < n$. We assume that the matrix $\X^*$ can be decomposed as $\X^*=\L^*+\S^*$, where $\L^*$ is a low rank matrix of rank $r << \min(n,q)$ and $\S^*$ is a sparse matrix. Each column of $\S$ contains $\rho$ non-zero entries. The matrices $\A_k$ are known and mutually independent for different $k$. To address this recovery problem, we propose a novel fast GD-based solution called AltGDmin-LR+S, which is memory and communication efficient. We numerically evaluate its performance by conducting a detailed simulation-based study.
摘要
Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging
paper_authors: Huidong Xie, Weijie Gan, Bo Zhou, Xiongchao Chen, Qiong Liu, Xueqi Guo, Liang Guo, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Chi Liu for: 降低PET扫描中的辐射剂量是一个重要的话题，这种方法可以减少辐射风险和辐射暴露。methods: 该研究使用了扩散模型，这种新的生成器模型已经在医疗影像领域中得到了广泛的应用，并且在生成高质量样本方面表现出了强大的潜力。results: 该研究表明，提出了一种名为DDPET的新方法，该方法可以同时降低PET图像的噪声水平和辐射剂量。DDPET方法在395名患者的数据上进行了测试，并与先前的扩散模型和噪声意识的医疗图像降噪方法进行比较。结果显示，DDPET方法在3D低剂量PET扫描中表现出了superior的性能。Abstract
As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for various tasks in medical imaging. However, it is difficult to extend diffusion models for 3D image reconstructions due to the memory burden. Directly stacking 2D slices together to create 3D image volumes would results in severe inconsistencies between slices. Previous works tried to either applying a penalty term along the z-axis to remove inconsistencies or reconstructing the 3D image volumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless, these previous methods failed to produce satisfactory results in challenging cases for PET image denoising. In addition to administered dose, the noise-levels in PET images are affected by several other factors in clinical settings, such as scan time, patient size, and weight, etc. Therefore, a method to simultaneously denoise PET images with different noise-levels is needed. Here, we proposed a dose-aware diffusion model for 3D low-dose PET imaging (DDPET) to address these challenges. The proposed DDPET method was tested on 295 patients from three different medical institutions globally with different low-dose levels. These patient data were acquired on three different commercial PET scanners, including Siemens Vision Quadra, Siemens mCT, and United Imaging Healthcare uExplorere. The proposed method demonstrated superior performance over previously proposed diffusion models for 3D imaging problems as well as models proposed for noise-aware medical image denoising. Code is available at: xxx.
摘要
As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. Recently, diffusion models have emerged as the new state-of-the-art generative model to generate high-quality samples and have demonstrated strong potential for various tasks in medical imaging. However, it is difficult to extend diffusion models for 3D image reconstructions due to the memory burden. Directly stacking 2D slices together to create 3D image volumes would results in severe inconsistencies between slices. Previous works tried to either applying a penalty term along the z-axis to remove inconsistencies or reconstructing the 3D image volumes with 2 pre-trained perpendicular 2D diffusion models. Nonetheless, these previous methods failed to produce satisfactory results in challenging cases for PET image denoising. In addition to administered dose, the noise-levels in PET images are affected by several other factors in clinical settings, such as scan time, patient size, and weight, etc. Therefore, a method to simultaneously denoise PET images with different noise-levels is needed. Here, we proposed a dose-aware diffusion model for 3D low-dose PET imaging (DDPET) to address these challenges. The proposed DDPET method was tested on 295 patients from three different medical institutions globally with different low-dose levels. These patient data were acquired on three different commercial PET scanners, including Siemens Vision Quadra, Siemens mCT, and United Imaging Healthcare uExplorere. The proposed method demonstrated superior performance over previously proposed diffusion models for 3D imaging problems as well as models proposed for noise-aware medical image denoising. Code is available at: xxx.Simplified Chinese:随着PET成像受到了辐射暴露和癌症风险，降低PET扫描中的辐射剂量是一项非常重要的话题。当前，扩散模型在医学成像中表现出了很强的潜力，但是将扩散模型扩展到3D图像重建中具有很大的内存压力。直接将2D slice合并成3D图像卷积会导致 slice之间的差异变得严重。先前的方法包括应用z轴方向的罚因项来消除差异或者使用2个垂直的 pré-trained 2D扩散模型来重建3D图像。然而，这些方法在复杂的PET图像去噪问题上未能实现满意的结果。医学设置中的辐射剂量以外，PET图像的噪声水平还受到了扫描时间、病人体重、体重等多个因素的影响。因此，一种同时去噪PET图像不同噪声水平的方法是需要的。我们提出了一种考虑辐射剂量的扩散模型，以解决这些挑战。我们的DDPET方法在295名患者的数据上进行了测试，这些患者数据来自全球三个不同的医疗机构，并在三个不同的商业PET扫描仪上获得。我们的方法在3D扫描问题上表现出了较好的性能，并且在医学去噪问题上也表现出了优于之前的扩散模型和噪声意识的医学图像去噪方法。代码可以在: xxx 上获取。