results: 这个研究发现，DL-SR方法可以在不同的干扰模型下，对低分辨率（LR）微缩endooscopic图像进行超解析，并且可以提高传统的图像质量指标。此外，endooscopist的解释也与高分辨率图像相似。Abstract
Significance: Endoscopic screening for esophageal cancer may enable early cancer diagnosis and treatment. While optical microendoscopic technology has shown promise in improving specificity, the limited field of view (<1 mm) significantly reduces the ability to survey large areas efficiently in esophageal cancer screening. Aim: To improve the efficiency of endoscopic screening, we proposed a novel end-expandable endoscopic optical fiber probe for larger field of visualization and employed a deep learning-based image super-resolution (DL-SR) method to overcome the issue of limited sampling capability. Approach: To demonstrate feasibility of the end-expandable optical fiber probe, DL-SR was applied on simulated low-resolution (LR) microendoscopic images to generate super-resolved (SR) ones. Varying the degradation model of image data acquisition, we identified the optimal parameters for optical fiber probe prototyping. The proposed screening method was validated with a human pathology reading study. Results: For various degradation parameters considered, the DL-SR method demonstrated different levels of improvement of traditional measures of image quality. The endoscopist interpretations of the SR images were comparable to those performed on the high-resolution ones. Conclusions: This work suggests avenues for development of DL-SR-enabled end-expandable optical fiber probes to improve high-yield esophageal cancer screening.
摘要
significans：endoscopic screening for esophageal cancer may enable early cancer diagnosis and treatment。although optical microendoscopic technology has shown promise in improving specificity，the limited field of view (<1 mm) significantly reduces the ability to survey large areas efficiently in esophageal cancer screening。 aim：to improve the efficiency of endoscopic screening，we proposed a novel end-expandable endoscopic optical fiber probe for larger field of visualization and employed a deep learning-based image super-resolution (DL-SR) method to overcome the issue of limited sampling capability。approach：to demonstrate feasibility of the end-expandable optical fiber probe，DL-SR was applied on simulated low-resolution (LR) microendoscopic images to generate super-resolved (SR) ones。varying the degradation model of image data acquisition，we identified the optimal parameters for optical fiber probe prototyping。the proposed screening method was validated with a human pathology reading study。results：for various degradation parameters considered，the DL-SR method demonstrated different levels of improvement of traditional measures of image quality。the endoscopist interpretations of the SR images were comparable to those performed on the high-resolution ones。conclusion：this work suggests avenues for development of DL-SR-enabled end-expandable optical fiber probes to improve high-yield esophageal cancer screening。
Detecting internal disorders in fruit by CT. Part 1: Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit
paper_authors: Dirk Elias Schut, Rachael Maree Wood, Anna Katharina Trull, Rob Schouten, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg for: This paper aims to create a dataset of image pairs of photographs of apple slices and their corresponding CT slices to study internal disorders in apples using CT imaging.methods: The workflow includes data acquisition, image segmentation, image registration, and validation methods. The image registration method aligns all available slices of an apple within a single optimization problem, assuming that the slices are parallel.results: The dataset was acquired from 107 ‘Kanzi’ apples that had been stored in controlled atmosphere (CA) storage for 8 months. In this dataset, the distance between annotations in the slice photograph and the matching CT slice was, on average, $1.47 \pm 0.40$ mm.Abstract
A large percentage of apples are affected by internal disorders after long-term storage, which makes them unacceptable in the supply chain. CT imaging is a promising technique for in-line detection of these disorders. Therefore, it is crucial to understand how different disorders affect the image features that can be observed in CT scans. This paper presents a workflow for creating datasets of image pairs of photographs of apple slices and their corresponding CT slices. By having CT and photographic images of the same part of the apple, the complementary information in both images can be used to study the processes underlying internal disorders and how internal disorders can be measured in CT images. The workflow includes data acquisition, image segmentation, image registration, and validation methods. The image registration method aligns all available slices of an apple within a single optimization problem, assuming that the slices are parallel. This method outperformed optimizing the alignment separately for each slice. The workflow was applied to create a dataset of 1347 slice photographs and their corresponding CT slices. The dataset was acquired from 107 'Kanzi' apples that had been stored in controlled atmosphere (CA) storage for 8 months. In this dataset, the distance between annotations in the slice photograph and the matching CT slice was, on average, $1.47 \pm 0.40$ mm. Our workflow allows collecting large datasets of accurately aligned photo-CT image pairs, which can help distinguish internal disorders with a similar appearance on CT. With slight modifications, a similar workflow can be applied to other fruits or MRI instead of CT scans.
摘要
大量的苹果在长期存储后会受到内部疾病的影响，导致它们不符合供应链的标准。CT成像是一种有前途的技术，可以在生产过程中检测内部疾病。因此，了解不同的内部疾病如何影响CT成像中可见的特征非常重要。这篇论文提出了一个工作流程，用于创建包含图像对的数据集。通过对图像进行分割、注册和验证，可以利用图像之间的相似性来研究内部疾病的过程和CT成像中的疾病识别。工作流程包括数据采集、图像分割、图像注册和验证方法。图像注册方法将所有可用的苹果片归类到单一优化问题中，假设苹果片是平行的。这种方法在每个苹果片上进行优化时表现出色。工作流程应用于创建了1347个图像对，其中每个对包含一个苹果片的相片和相应的CT成像。这些数据来自于在控制气候存储8个月后采集的107个'Kanzi'苹果。在这个数据集中，相片和CT成像之间的距离平均为1.47±0.40毫米。我们的工作流程可以生成大量准确匹配的相片-CT成像对，帮助分辨CT成像中的内部疾病。通过小幅修改，这种工作流程可以应用于其他水果或MRI等。
Spectro-spatial hyperspectral image reconstruction from interferometric acquisitions
results: 研究人员在这篇论文中提出了一种将空间常量约束添加到减除过程中，以提高图像征化的性能。这种方法与传统的像素级减除方法相比，能够更好地捕捉图像的空间结构信息。Abstract
In the last decade, novel hyperspectral cameras have been developed with particularly desirable characteristics of compactness and short acquisition time, retaining their potential to obtain spectral/spatial resolution competitive with respect to traditional cameras. However, a computational effort is required to recover an interpretable data cube. In this work we focus our attention on imaging spectrometers based on interferometry, for which the raw acquisition is an image whose spectral component is expressed as an interferogram. Previous works have focused on the inversion of such acquisition on a pixel-by-pixel basis within a Bayesian framework, leaving behind critical information on the spatial structure of the image data cube. In this work, we address this problem by integrating a spatial regularization for image reconstruction, showing that the combination of spectral and spatial regularizers leads to enhanced performances with respect to the pixelwise case. We compare our results with Plug-and-Play techniques, as its strategy to inject a set of denoisers from the literature can be implemented seamlessly with our physics-based formulation of the optimization problem.
摘要
过去一个十年，新的干涉 hyperspectral 镜头已经开发出来，具有特别的紧凑性和捕获时间短的特点，保留了它们对传统镜头的spectral/空间分辨率的竞争力。然而，需要计算的努力来回归一个可解释的数据立方体。在这种工作中，我们关注干涉spectrometer，其捕获数据的原始形式是一个干涉ogram。前一些工作是在每个像素基础上进行bayesian框架中进行干涉的减少，留下了图像数据立方体的空间结构信息。在这种工作中，我们解决这个问题，通过 интеGRATE一个空间规则来进行图像重建，表明将spectral和空间规则相结合会提高对于像素基础的情况的性能。我们与插入技术相比较，因为它们的策略可以轻松地与我们的物理基础的优化问题进行结合。