paper_authors: Li Chen, Jonathan Rubin, Jiahong Ouyang, Naveen Balaraju, Shubham Patil, Courosh Mehanian, Sourabh Kulhare, Rachel Millin, Kenton W Gregory, Cynthia R Gregory, Meihua Zhu, David O Kessler, Laurie Malia, Almaz Dessie, Joni Rabiner, Di Coneybeare, Bo Shopsin, Andrew Hersh, Cristian Madar, Jeffrey Shupp, Laura S Johnson, Jacob Avila, Kristin Dwyer, Peter Weimersheimer, Balasundar Raju, Jochen Kruecker, Alvin Chen
results: 我们使用超过27k个lung医疗超音波影像资料，来评估我们的方法。结果显示，我们的方法可以明显改善下测地点和类别lung混合的标注数据。相比基准模型，我们的方法尤其有利于有限的标注数据（例如只有5%的训练集）。Abstract
Self-supervised learning (SSL) methods have shown promise for medical imaging applications by learning meaningful visual representations, even when the amount of labeled data is limited. Here, we extend state-of-the-art contrastive learning SSL methods to 2D+time medical ultrasound video data by introducing a modified encoder and augmentation method capable of learning meaningful spatio-temporal representations, without requiring constraints on the input data. We evaluate our method on the challenging clinical task of identifying lung consolidations (an important pathological feature) in ultrasound videos. Using a multi-center dataset of over 27k lung ultrasound videos acquired from over 500 patients, we show that our method can significantly improve performance on downstream localization and classification of lung consolidation. Comparisons against baseline models trained without SSL show that the proposed methods are particularly advantageous when the size of labeled training data is limited (e.g., as little as 5% of the training set).
摘要
自我指导学习（SSL）方法在医疗影像应用中显示了承诺，通过学习有意义的视觉表示，即使有限量的标注数据。在这里，我们扩展了现状最佳的对比学习SSL方法，用于2D+时医疗超音波视频数据。我们引入了修改后的编码器和扩展方法，能够学习有意义的空间-时表示，不需要输入数据的约束。我们使用了多中心的肺超音波视频数据集，包含了超过27k个肺超音波视频，从超过500名患者中收集到。我们显示了我们的方法可以明显提高肺混凝级地址和分类性能。相比基准模型未使用SSL训练，我们的方法在标注数据有限时表现特别有利。
X-ray phase and dark-field computed tomography without optical elements
paper_authors: T. A. Leatham, D. M. Paganin, K. S. Morgan
for: The paper is written for researchers and practitioners in the field of X-ray imaging, particularly those interested in phase and dark-field computed tomography.
methods: The paper presents a new algorithm for X-ray diffuse dark-field imaging based on the x-ray Fokker-Planck equation, which can reconstruct both the sample density and dark-field/diffusion properties in 3D with high spatial resolution.
results: The proposed algorithm can be used to reconstruct both the sample density and dark-field Fokker-Planck diffusion coefficients with only two sample exposures at each projection angle, making it a valuable tool for biomedical imaging and industrial settings.Here’s the same information in Simplified Chinese text:
results: 该算法只需要两个样品曝光角度的样品曝光，就可以成功地重建样品的密度和黑场福克-朋克扩散系数。Abstract
X-ray diffusive dark-field imaging, which allows spatially unresolved microstructure to be mapped across a sample, is an increasingly popular tool in an array of settings. Here, we present a new algorithm for phase and dark-field computed tomography based on the x-ray Fokker-Planck equation. Needing only a coherent x-ray source, sample, and detector, our propagation-based algorithm can map the sample density and dark-field/diffusion properties of the sample in 3D. Importantly, incorporating dark-field information in the density reconstruction process enables a higher spatial resolution reconstruction than possible with previous propagation-based approaches. Two sample exposures at each projection angle are sufficient for the successful reconstruction of both the sample density and dark-field Fokker-Planck diffusion coefficients. We anticipate that the proposed algorithm may be of benefit in biomedical imaging and industrial settings.
摘要
PC-bzip2: a phase-space continuity enhanced lossless compression algorithm for light field microscopy data
methods: 我们提出了一种基于 GPU 和多核心 CPU 的高速无损压缩方法，它结合了快速Entropy 评估和高速无损压缩。
results: 我们的方法可以在不同 SNR 下实现约 10% 的压缩率提升，同时保持高速压缩能力，并且在时间序列数据上表现出superior的压缩率。Abstract
Light-field fluorescence microscopy (LFM) is a powerful elegant compact method for long-term high-speed imaging of complex biological systems, such as neuron activities and rapid movements of organelles. LFM experiments typically generate terabytes image data and require a huge number of storage space. Some lossy compression algorithms have been proposed recently with good compression performance. However, since the specimen usually only tolerates low power density illumination for long-term imaging with low phototoxicity, the image signal-to-noise ratio (SNR) is relative-ly low, which will cause the loss of some efficient position or intensity information by using such lossy compression al-gorithms. Here, we propose a phase-space continuity enhanced bzip2 (PC-bzip2) lossless compression method for LFM data as a high efficiency and open-source tool, which combines GPU-based fast entropy judgement and multi-core-CPU-based high-speed lossless compression. Our proposed method achieves almost 10% compression ratio improvement while keeping the capability of high-speed compression, compared with original bzip2. We evaluated our method on fluorescence beads data and fluorescence staining cells data with different SNRs. Moreover, by introducing the temporal continuity, our method shows the superior compression ratio on time series data of zebrafish blood vessels.
摘要
光场液体微镜技术（LFM）是一种 poderful yet elegant compact方法 для长期高速成像复杂生物系统，如神经活动和迅速运动的组织质量。LFM实验通常生成terabytes的图像数据，需要巨量的存储空间。一些lossy压缩算法已经被提出，但是由于样品通常只能承受低功率照明的长期成像，图像信号噪比（SNR）相对较低，这会导致使用such lossy压缩算法中的效率信息丢失。在这里，我们提出了一种phasel Space Continuity Enhanced bzip2（PC-bzip2）无损压缩方法为LFM数据，这是一种高效性和开源工具，它结合GPU基于快速权重评估和多核CPU基于高速无损压缩。我们的提议方法与原始bzip2的 compressión ratio improvement为9.8%，同时保持高速压缩的能力。我们对fluorescence beads数据和fluorescence染料细胞数据进行了不同SNR的评估，并且通过引入时间连续性，我们的方法在时间序列数据上实现了更高的压缩率。