Most Cited Articles

Tuberculosis is a major health threat in many regions of the world. Opportunistic infections in immunocompromised HIV/AIDS patients and multi-drug-resistant bacterial strains have exacerbated the problem, while diagnosing tuberculosis still remains a challenge. When left undiagnosed and thus untreated, mortality rates of patients with tuberculosis are high. Standard diagnostics still rely on methods developed in the last century. They are slow and often unreliable. In an effort to reduce the burden of the disease, this paper presents our automated approach for detecting tuberculosis in conventional posteroanterior chest radiographs. We first extract the lung region using a graph cut segmentation method. For this lung region, we compute a set of texture and shape features, which enable the X-rays to be classified as normal or abnormal using a binary classifier. We measure the performance of our system on two datasets: a set collected by the tuberculosis control program of our local county's health department in the United States, and a set collected by Shenzhen Hospital, China. The proposed computer-aided diagnostic system for TB screening, which is ready for field deployment, achieves a performance that approaches the performance of human experts. We achieve an area under the ROC curve (AUC) of 87% (78.3% accuracy) for the first set, and an AUC of 90% (84% accuracy) for the second set. For the first set, we compare our system performance with the performance of radiologists. When trying not to miss any positive cases, radiologists achieve an accuracy of about 82% on this set, and their false positive rate is about half of our system's rate.

结核病在全球许多地区构成重大健康威胁。免疫功能低下的HIV/AIDS患者中的机会性感染以及耐多药菌株加剧了这一问题，而结核病的诊断仍然具有挑战性。一旦未确诊因而未治疗，结核病患者的死亡率很高。标准诊断仍依赖于上世纪开发的方法，这些方法缓慢且常常不可靠。为减轻疾病负担，本文提出了一种在常规后前位胸部X光片中自动检测结核病的方法。我们首先使用图割分割方法提取肺部区域。针对该肺部区域，我们计算一组纹理和形状特征，从而使用二元分类器将X光片分为正常或异常。我们在两个数据集上测量系统的性能：一组由美国本地县卫生部门的结核病控制项目收集，另一组由中国深圳医院收集。所提出的用于结核病筛查的计算机辅助诊断系统已准备好进行现场部署，其性能接近人类专家的水平。我们在第一个数据集上实现了ROC曲线下面积（AUC）87%（准确率78.3%），在第二个数据集上实现了AUC 90%（准确率84%）。对于第一个数据集，我们将系统性能与放射科医生的性能进行了比较。在不遗漏任何阳性病例的情况下，放射科医生在该数据集上的准确率约为82%，其假阳性率约为我们系统的一半。

We describe a method for automatically building statistical shape models from a training set of example boundaries/surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences between all members of a set of training shapes. Often this is achieved by locating a set of "landmarks" manually on each training image, which is time consuming and subjective in two dimensions and almost impossible in three dimensions. We describe how shape models can be built automatically by posing the correspondence problem as one of finding the parameterization for each shape in the training set. We select the set of parameterizations that build the "best" model. We define "best" as that which minimizes the description length of the training set, arguing that this leads to models with good compactness, specificity and generalization ability. We show how a set of shape parameterizations can be represented and manipulated in order to build a minimum description length model. Results are given for several different training sets of two-dimensional boundaries, showing that the proposed method constructs better models than other approaches including manual landmarking-the current gold standard. We also show that the method can be extended straightforwardly to three dimensions.

我们描述了一种从示例边界/表面训练集自动构建统计形状模型的方法。这些模型作为分割和解释图像的基础显示出相当大的前景。然而，该方法的一个缺点是需要在一组训练形状的所有成员之间建立一组密集的对应关系。这通常是通过在每个训练图像上手动定位一组“地标”来实现的，这在二维中耗时且主观，在三维中几乎不可能。我们描述了如何通过将对应问题转化为寻找训练集中每个形状的参数化来自动构建形状模型。我们选择构建“最佳”模型的参数化集合。我们将“最佳”定义为最小化训练集描述长度的参数化，认为这会导致模型具有良好的紧凑性、特异性和泛化能力。我们展示了如何表示和操作一组形状参数化以构建最小描述长度模型。给出了几个不同二维边界训练集的结果，表明所提出的方法构建的模型优于其他方法，包括当前金标准的手动地标定位。我们还表明该方法可以直接扩展到三维。

We describe an automated method to locate the optic nerve in images of the ocular fundus. Our method uses a novel algorithm we call fuzzy convergence to determine the origination of the blood vessel network. We evaluate our method using 31 images of healthy retinas and 50 images of diseased retinas, containing such diverse symptoms as tortuous vessels, choroidal neovascularization, and hemorrhages...

我们描述了一种自动定位眼底图像中视神经的方法。该方法使用一种我们称之为模糊收敛的新算法来确定血管网络的起点。我们使用31张健康视网膜图像和50张患病视网膜图像评估了该方法，这些图像包含各种症状，如迂曲血管、脉络膜新生血管和出血等。

Active model-based segmentation has frequently been used in medical image processing with considerable success. Although the active model-based method was initially viewed as an optimization problem, most researchers implement it as a partial differential equation solution. The advantages and disadvantages of the active model-based method are distinct: speed and stability. To improve its performan...

基于活动模型的图像分割在医学图像处理中经常被使用，并取得了相当大的成功。尽管基于活动模型的方法最初被视为优化问题，但大多数研究人员将其实现为偏微分方程解。基于活动模型的方法的优缺点很明显：速度和稳定性。为了提高其性能...

Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promisin...

将关于器官形状和位置的先验知识纳入是提高图像分析方法性能的关键。特别是，在图像因采集限制而损坏并包含伪影的情况下，先验知识非常有用。学习技术能够很好地捕捉解剖对象的高度约束特性。然而，在最近和最有希望...

The traditional chest radiograph is still ubiquitous in clinical practice, and will likely remain so for quite some time. Yet, its interpretation is notoriously difficult. This explains the continued interest in computer-aided diagnosis for chest radiography. The purpose of this survey is to categorize and briefly review the literature on computer analysis of chest images, which comprises over 150 papers published in the last 30 years. Remaining challenges are indicated and some directions for future research are given.

传统的胸部X线照片在临床实践中仍然无处不在，并且很可能在相当长的一段时间内保持这种状态。然而，其解读是出了名的困难。这解释了对计算机辅助诊断在胸部X线摄影中持续的兴趣。本综述的目的是对关于胸部图像计算机分析的文献进行分类和简要回顾，这包括过去30年发表的150多篇论文。指出了剩余的挑战，并给出了一些未来研究的方向。

The problem of reconstruction in positron emission tomography (PET) is basically estimating the number of photon pairs emitted from the source. Using the concept of the maximum-likelihood (ML) algorithm, the problem of reconstruction is reduced to determining an estimate of the emitter density that maximizes the probability of observing the actual detector count data over all possible emitter dens...

中文摘要翻译待生成

Large-tip-angle multidimensional radio-frequency (RF) pulse design is a difficult problem, due to the nonlinear response of magnetization to applied RF at large tip-angles. In parallel excitation, multidimensional RF pulse design is further complicated by the possibility for transmit field patterns to change between subjects, requiring pulses to be designed rapidly while a subject lies in the scan...

大翻转角多维射频（RF）脉冲设计是一个难题，因为在大翻转角下磁化对施加RF的响应是非线性的。在并行激励中，由于发射场模式可能在不同受试者之间变化，需要受试者在扫描过程中快速设计脉冲，这使得多维RF脉冲设计更加复杂。

This paper presents a robust and fully automatic filter-based approach for retinal vessel segmentation. We propose new filters based on 3D rotating frames in so-called orientation scores, which are functions on the Lie-group domain of positions and orientations ℝ 2 × S 1 . By means of a wavelet-type transform, a 2D image is lifted to a 3D orientation score, where elongated structures are disentangled into their corresponding orientation planes. In the lifted domain ℝ 2 × S 1 , vessels are enhanced by means of multi-scale second-order Gaussian derivatives perpendicular to the line structures. More precisely, we use a left-invariant rotating derivative (LID) frame, and a locally adaptive derivative (LAD) frame. The LAD is adaptive to the local line structures and is found by eigensystem analysis of the left-invariant Hessian matrix (computed with the LID). After multi-scale filtering via the LID or LAD in the orientation score domain, the results are projected back to the 2D image plane giving us the enhanced vessels. Then a binary segmentation is obtained through thresholding. The proposed methods are validated on six retinal image datasets with different image types, on which competitive segmentation performances are achieved. In particular, the proposed algorithm of applying the LAD filter on orientation scores (LAD-OS) outperforms most of the state-of-the-art methods. The LAD-OS is capable of dealing with typically difficult cases like crossings, central arterial reflex, closely parallel and tiny vessels. The high computational speed of the proposed methods allows processing of large datasets in a screening setting.

本文提出了一种鲁棒且全自动的基于滤波器的视网膜血管分割方法。我们提出了基于所谓方向得分中的3D旋转框架的新滤波器，方向得分是位置和方向李群域ℝ²×S¹上的函数。通过小波型变换，将2D图像提升到3D方向得分，其中细长结构被分解到其对应的方向平面中。在提升域ℝ²×S¹中，通过垂直于线结构的多尺度二阶高斯导数增强血管。更精确地说，我们使用了左不变旋转导数（LID）框架和局部自适应导数（LAD）框架。LAD对局部线结构自适应，通过左不变Hessian矩阵（使用LID计算）的特征系统分析得到。通过在方向得分域中使用LID或LAD进行多尺度滤波后，结果投影回2D图像平面，得到增强的血管。然后通过阈值化得到二值分割。所提出的方法在六个具有不同图像类型的视网膜图像数据集上进行了验证，取得了具有竞争力的分割性能。特别是，所提出的在方向得分上应用LAD滤波器（LAD-OS）的算法优于大多数现有方法。LAD-OS能够处理典型困难情况，如交叉、中央动脉反射、紧密平行和微小的血管。所提出的方法计算速度快，允许在筛选设置中处理大型数据集。

A comparison of six similarity measures for use in intensity-based two-dimensional-three-dimensional (2-D-3-D) image registration is presented. The accuracy of the similarity measures are compared to a "gold-standard" registration which has been accurately calculated using fiducial markers. The similarity measures are used to register a computed tomography (CT) scan of a spine phantom to a fluoroscopy image of the phantom. The registration is carried out within a region-of-interest in the fluoroscopy image which is user defined to contain a single vertebra. Many of the problems involved in this type of registration are caused by features which were not modeled by a phantom image alone. More realistic "gold-standard" data sets were simulated using the phantom image with clinical image features overlaid. Results show that the introduction of soft-tissue structures and interventional instruments into the phantom image can have a large effect on the performance of some similarity measures previously applied to 2-D-3-D image registration. Two measures were able to register accurately and robustly even when soft-tissue structures and interventional instruments were present as differences between the images. These measures were pattern intensity and gradient difference. Their registration accuracy, for all the rigid-body parameters except for the source to film translation, was within a root-mean-square (rms) error of 0.53 mm or degrees to the "gold-standard" values. No failures occurred while registering using these measures.

本文比较了六种基于强度的二维-三维（2-D-3-D）图像配准中的相似性度量。这些相似性度量的精度与使用基准标记精确计算的金标准配准进行了比较。这些相似性度量用于将脊柱模型的计算机断层扫描（CT）图像注册到荧光透视图像...

A new imaging modality is introduced to image electrical conductivity of biological tissues via contactless measurements. This modality uses magnetic excitation to induce currents inside the body and measures the magnetic fields of the induced currents. In this study, the mathematical basis of the methodology is analyzed and numerical models are developed to simulate the imaging system. The induce...

介绍了一种新的成像模态，通过非接触测量对生物组织的电导率进行成像。该模态利用磁激励在体内感应电流，并测量感应电流产生的磁场。本研究分析了该方法的数学基础，并开发了数值模型来模拟成像系统。感应...

Bone age rating is associated with a considerable variability from the human interpretation, and this is the motivation for presenting a new method for automated determination of bone age (skeletal maturity). The method, called BoneXpert, reconstructs, from radiographs of the hand, the borders of 15 bones automatically and then computes ldquointrinsicrdquo bone ages for each of 13 bones (radius, ulna, and 11 short bones). Finally, it transforms the intrinsic bone ages into Greulich Pyle (GP) or Tanner Whitehouse (TW) bone age. The bone reconstruction method automatically rejects images with abnormal bone morphology or very poor image quality. From the methodological point of view, BoneXpert contains the following innovations: 1) a generative model (active appearance model) for the bone reconstruction; 2) the prediction of bone age from shape, intensity, and texture scores derived from principal component analysis; 3) the consensus bone age concept that defines bone age of each bone as the best estimate of the bone age of the other bones in the hand; 4) a common bone age model for males and females; and 5) the unified modelling of TW and GP bone age. BoneXpert is developed on 1559 images. It is validated on the Greulich Pyle atlas in the age range 2-17 years yielding an SD of 0.42 years [0.37; 0.47] 95% conf, and on 84 clinical TW-rated images yielding an SD of 0.80 years [0.68; 0.93] 95% conf. The precision of the GP bone age determination (its ability to yield the same result on a repeated radiograph) is inferred under suitable assumptions from six longitudinal series of radiographs. The result is an SD on a single determination of 0.17 years [0.13; 0.21] 95% conf.

骨龄评分与人类解释存在相当大的变异性，这促使我们提出一种新的自动确定骨龄（骨骼成熟度）的方法。该方法称为BoneXpert，从手部X光片自动重建15块骨的边界，然后计算13块骨（桡骨、u...）的“内在”骨龄。

Cancer diagnosis, prognosis, mymargin and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data. However, most deep learning-based objective outcome prediction and grading paradigms are based on histology or genomics alone and do not make use of the complementary information in an intuitive manner. In this work, we propose Pathomic Fusion , an interpretable strategy for end-to-end multimodal fusion of histology image and genomic (mutations, CNV, RNA-Seq) features for survival outcome prediction. Our approach models pairwise feature interactions across modalities by taking the Kronecker product of unimodal feature representations, and controls the expressiveness of each representation via a gating-based attention mechanism. Following supervised learning, we are able to interpret and saliently localize features across each modality, and understand how feature importance shifts when conditioning on multimodal input. We validate our approach using glioma and clear cell renal cell carcinoma datasets from the Cancer Genome Atlas (TCGA), which contains paired whole-slide image, genotype, and transcriptome data with ground truth survival and histologic grade labels. In a 15-fold cross-validation, our results demonstrate that the proposed multimodal fusion paradigm improves prognostic determinations from ground truth grading and molecular subtyping, as well as unimodal deep networks trained on histology and genomic data alone. The proposed method establishes insight and theory on how to train deep networks on multimodal biomedical data in an intuitive manner, which will be useful for other problems in medicine that seek to combine heterogeneous data streams for understanding diseases and predicting response and resistance to treatment. Code and trained models are made available at: https://github.com/mahmoodlab/PathomicFusion .

中文摘要翻译待生成

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentatio...

乳腺X线摄影风险评分通常通过从乳腺X线照片中提取一组手工特征，并将响应直接或间接与乳腺癌风险相关联来实现自动化。我们提出了一种从未标记数据中学习特征层次结构的方法。当学习到的特征被用作简单分类器的输入时，可以解决两个不同的任务：i）乳腺密度分割...

Fractal analysis of bone X-ray images has received much interest recently for the diagnosis of bone disease. Here, the authors propose a fractal analysis of bone X-ray tomographic microscopy (XTM) projections. The aim of the study is to establish whether or not there is a correlation between three-dimensional (3-D) trabecular changes and two-dimensional (2-D) fractal descriptors. Using a highly co...

骨X射线图像的分形分析近来在骨病诊断中受到广泛关注。本文作者提出对骨X射线断层显微镜（XTM）投影进行分形分析。研究旨在确定三维（3-D）小梁变化与二维（2-D）分形描述符之间是否存在相关性。利用高度共...

The advent of digital pathology provides us with the challenging opportunity to automatically analyze whole slides of diseased tissue in order to derive quantitative profiles that can be used for diagnosis and prognosis tasks. In particular, for the development of interpretable models, the detection and segmentation of cell nuclei is of the utmost importance. In this paper, we describe a new metho...

数字病理学的出现为我们提供了自动分析病变组织全切片以获得可用于诊断和预后任务的定量图谱的挑战性机会。特别是，对于可解释模型的开发，细胞核的检测和分割至关重要。在本文中，我们描述了一种新的方法...

We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans—with manually segmented white matter, cerebral cortex, ventricles and subcortical structures—to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.

我们提出了一种非参数、概率模型，用于在给定一组训练图像和对应标签图的情况下自动分割医学图像。由此产生的推理算法依赖于测试图像与单个训练图像之间的成对配准。然后将训练标签转移到测试图像并融合，以计算测试对象的最终分割。这种标签融合方法已被证明能产生准确的分割，因为使用多个配准能够捕获更大的受试者间解剖变异性，并提高对偶发配准失败的鲁棒性。据我们所知，本文首次提出了一个全面的概率框架，严格地证明了标签融合作为一种分割方法的合理性。所提出的框架使我们能够在理论上和实践上比较不同的标签融合算法。特别是，最近的标签融合或多图谱分割算法被解释为我们框架的特例。我们进行了两组实验来验证所提出的方法。在第一组实验中，我们使用39个脑部MRI扫描（具有手动分割的白质、大脑皮层、脑室和皮层下结构）来比较不同的标签融合算法和广泛使用的FreeSurfer全脑分割工具。我们的结果表明，所提出的框架比FreeSurfer和之前的标签融合算法产生更准确的分割。在第二个实验中，我们使用282名受试者的脑部MRI扫描，证明所提出的分割工具足够敏感，能够在衰老和阿尔茨海默病的研究中稳健地检测海马体积变化。

We introduce a novel algorithm to reconstruct dynamic magnetic resonance imaging (MRI) data from under-sampled k-t space data. In contrast to classical model based cine MRI schemes that rely on the sparsity or banded structure in Fourier space, we use the compact representation of the data in the Karhunen Louve transform (KLT) domain to exploit the correlations in the dataset. The use of the data-...

我们提出了一种新颖的算法，用于从欠采样的k-t空间数据重建动态磁共振成像（MRI）数据。与依赖于傅里叶空间中稀疏性或带状结构的经典基于模型的电影MRI方案不同，我们利用数据在Karhunen-Loève变换（KLT）域中的紧凑表示来挖掘数据集中的相关性。使用数据-...

Considers the direct Fourier methods (DFM's) for reconstructing an image from its given X-ray projections. The main purpose here is to use concepts from numerical analysis to estimate the errors in the methods. The authors also suggest an alternative to the interpolations involved in the DFM's and estimate the number of terms involved. Realizing that one of the main reasons for the degradation of ...

考虑使用直接傅里叶方法（DFM）从给定的X射线投影重建图像。这里的主要目的是使用数值分析的概念来估计方法中的误差。作者还提出了DFM中涉及的插值的替代方案，并估计了涉及的项数。意识到...

Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data. Furthermore, we design a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels. We evaluated the effectiveness, efficiency, as well as the generalization capability of the proposed framework on two publicly available databases. One is from ISBI 2016 skin lesion analysis towards melanoma detection challenge, and the other is the PH2 database. Experimental results showed that the proposed method outperformed other state-of-the-art algorithms on these two databases. Our method is general enough and only needs minimum pre- and post-processing, which allows its adoption in a variety of medical image segmentation tasks.

在皮肤镜图像中自动分割皮肤病变是一项具有挑战性的任务，因为病变与周围皮肤之间的对比度低，病变边界不规则且模糊，存在各种伪影以及不同的成像采集条件。本文提出了一种全自动的皮肤病变分割方法，利用19层深度卷积神经网络进行端到端训练，无需依赖数据的先验知识。我们提出了一系列策略，以确保在有限的训练数据下实现有效且高效的学习。此外，我们设计了一种基于Jaccard距离的新型损失函数，以消除样本重新加权的需要——在使用交叉熵作为图像分割损失函数时，由于前景和背景像素数量严重不平衡，通常需要进行样本重新加权。我们在两个公开数据库上评估了所提框架的有效性、效率以及泛化能力。一个来自ISBI 2016皮肤病变分析挑战赛（针对黑色素瘤检测），另一个是PH2数据库。实验结果表明，所提方法在这两个数据库上优于其他最先进的算法。我们的方法足够通用，只需要最少的预处理和后处理，因此可以应用于各种医学图像分割任务。

Accurate detection of vessel boundaries is particularly important for a precise extraction of vasculatures in magnetic resonance angiography (MRA). In this paper, we propose the use of weighted local variance (WLV)-based edge detection scheme for vessel boundary detection in MRA. The proposed method is robust against changes of intensity contrast of edges and capable of giving high detection respo...

准确检测血管边界对于磁共振血管成像（MRA）中血管的精确提取尤为重要。本文提出使用基于加权局部方差（WLV）的边缘检测方案用于MRA中的血管边界检测。该方法对边缘强度对比度的变化具有鲁棒性，并且能够给出高检测响应...

A new method to reconstruct the elastic modulus of soft tissue subjected to an external static compression is presented. In this approach the Newton-Raphson method is used to vary a finite element (FE) model of the elasticity equations to fit, in a least squared sense, a set of axial tissue displacement fields estimated using a correlation technique applied to ultrasound signals. The ill-condition...

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This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimate...

本文提出一种全自动算法，用于从多光谱磁共振（MR）图像中分割多发性硬化（MS）病灶。该方法利用正常脑图像的随机模型进行基于强度的组织分类，同时将模型中无法很好解释的异常点检测为MS病灶。它校正了MR场不均匀性，并估计...

Accurate medical image segmentation is of great significance for computer aided diagnosis. Although methods based on convolutional neural networks (CNNs) have achieved good results, it is weak to model the long-range dependencies, which is very important for segmentation task to build global context dependencies. The Transformers can establish long-range dependencies among pixels by self-attention, providing a supplement to the local convolution. In addition, multi-scale feature fusion and feature selection are crucial for medical image segmentation tasks, which is ignored by Transformers. However, it is challenging to directly apply self-attention to CNNs due to the quadratic computational complexity for high-resolution feature maps. Therefore, to integrate the merits of CNNs, multi-scale channel attention and Transformers, we propose an efficient hierarchical hybrid vision Transformer (H2Former) for medical image segmentation. With these merits, the model can be data-efficient for limited medical data regime. The experimental results show that our approach exceeds previous Transformer, CNNs and hybrid methods on three 2D and two 3D medical image segmentation tasks. Moreover, it keeps computational efficiency in model parameters, FLOPs and inference time. For example, H2Former outperforms TransUNet by 2.29% in IoU score on KVASIR-SEG dataset with 30.77% parameters and 59.23% FLOPs.

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Magnetic particle imaging (MPI) is a promising new medical imaging tracer modality with potential applications in human angiography, cancer imaging, in vivo cell tracking, and inflammation imaging. Here we demonstrate both theoretically and experimentally that multidimensional MPI is a linear shift-invariant imaging system with an analytic point spread function. We also introduce a fast image reco...

磁粒子成像（MPI）是一种前景广阔的新型医学影像示踪剂模态，在人体血管造影、癌症成像、体内细胞追踪和炎症成像方面具有潜在应用。这里我们从理论和实验两方面证明，多维MPI是一种线性平移不变成像系统，具有解析点扩散函数。我们还介绍了一种快速的图像重建方法。