Volume 38, Issue 2

We introduce a model-based image reconstruction framework with a convolution neural network (CNN)-based regularization prior. The proposed formulation provides a systematic approach for deriving deep architectures for inverse problems with the arbitrary structure. Since the forward model is explicitly accounted for, a smaller network with fewer parameters is sufficient to capture the image information compared to direct inversion approaches. Thus, reducing the demand for training data and training time. Since we rely on end-to-end training with weight sharing across iterations, the CNN weights are customized to the forward model, thus offering improved performance over approaches that rely on pre-trained denoisers. Our experiments show that the decoupling of the number of iterations from the network complexity offered by this approach provides benefits, including lower demand for training data, reduced risk of overfitting, and implementations with significantly reduced memory footprint. We propose to enforce data-consistency by using numerical optimization blocks, such as conjugate gradients algorithm within the network. This approach offers faster convergence per iteration, compared to methods that rely on proximal gradients steps to enforce data consistency. Our experiments show that the faster convergence translates to improved performance, primarily when the available GPU memory restricts the number of iterations.

我们提出了一种基于模型的图像重建框架，采用卷积神经网络（CNN）正则化先验。该公式提供了一种系统的方法，用于推导任意结构逆问题的深度学习架构。由于正向模型被明确考虑，与直接反演方法相比，一个参数更少的小型网络就足以捕获图像信息，从而减少对训练数据和训练时间的需求。由于我们依赖跨迭代共享权重的端到端训练，CNN权重是根据正向模型定制的，因此相比依赖预训练去噪器的方法提供了更好的性能。我们的实验表明，这种方法将迭代次数与网络复杂性分离，带来了好处，包括降低训练数据需求、减少过拟合风险以及显著减少内存占用的实现。我们建议通过在网络内使用数值优化块（例如共轭梯度算法）来强制执行数据一致性。与依赖近端梯度步骤来强制执行数据一致性的方法相比，这种方法每次迭代收敛更快。我们的实验表明，更快的收敛转化为更好的性能，尤其是在可用GPU内存限制迭代次数时。

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...

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of whic...

自动检测淋巴结中的癌症转移灶有潜力改善患者的预后评估。为了公正比较用于此目的的算法，我们在墨尔本举办的IEEE国际生物医学成像研讨会2017会议上设立了CAMELYON17挑战赛。超过300名参与者在挑战赛网站上注册，其中

In a wide range of semantic segmentation tasks, fully convolutional neural networks (F-CNNs) have been successfully leveraged to achieve the state-of-the-art performance. Architectural innovations of F-CNNs have mainly been on improving spatial encoding or network connectivity to aid gradient flow. In this paper, we aim toward an alternate direction of recalibrating the learned feature maps adapti...

在广泛的语义分割任务中，全卷积神经网络已成功用于实现最先进的性能。全卷积网络的架构创新主要集中在改进空间编码或网络连接以辅助梯度流。在本文中，我们旨在向另一个方向，即自适应地重新校准学习到的特征图...

Reducing the exposure to X-ray radiation while maintaining a clinically acceptable image quality is desirable in various CT applications. To realize low-dose CT (LdCT) imaging, model-based iterative reconstruction (MBIR) algorithms are widely adopted, but they require proper prior knowledge assumptions in the sinogram and/or image domains and involve tedious manual optimization of multiple paramet...

在各种CT应用中，希望在保持临床可接受的图像质量的同时减少X射线辐射暴露。为了实现低剂量CT（LdCT）成像，基于模型的迭代重建（MBIR）算法被广泛采用，但它们需要在正弦图和/或图像域中适当的先验知识假设，并涉及多个参数的繁琐手动优化...

Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging problem and an active area of research. Particular challenges, similarly as in other segmentation problems, include the class-imbalance problem as well as confounding background in DCE-MR images. To address these issues, we propose a mask-guided hierarchical learning (MHL) framework f...

基于动态对比增强磁共振成像（DCE-MRI）的乳腺肿瘤分割是一个具有挑战性的问题，也是一个活跃的研究领域。类似于其他分割问题，特别的挑战包括类别不平衡问题以及DCE-MR图像中的混杂背景。为了解决这些问题，我们提出了一种掩膜引导的分层学习（MHL）框架...

In portable, 3-D, and ultra-fast ultrasound imaging systems, there is an increasing demand for the reconstruction of high-quality images from a limited number of radio-frequency (RF) measurements due to receiver (Rx) or transmit (Xmit) event sub-sampling. However, due to the presence of side lobe artifacts from RF sub-sampling, the standard beamformer often produces blurry images with less contras...

在便携式、3D和超快超声成像系统中，由于接收或发射事件次采样，从有限数量的射频测量中重建高质量图像的需求日益增加。然而，由于射频次采样导致的旁瓣伪影，标准波束形成器通常会产生模糊且对比度较低的图像...

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3-D, especially from late gadolinium-enhanced (LGE) magnetic resonance imaging, rely heavily on manual segmentation methods that are ...

房颤是最常见的心律失常形式。由于对直接维持房颤的潜在心房结构缺乏了解，目前的房颤治疗方法仍不理想。现有分析三维心房结构的方法，尤其是从晚期钆增强磁共振成像中，严重依赖于手动分割方法，这些方法...

Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In clinical routine, hundreds of images are acquired for each patient, which require automatic plaque detection for fast and accurate decision support. So...

冠心病是一种常见死因，尽管它是可预防的。为了治疗动脉壁中的斑块沉积，专家可以使用血管内光学相干断层扫描来检测和表征病变。在临床常规中，每个患者会获取数百张图像，因此需要自动斑块检测以提供快速准确的决策支持。所以...

With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3-D rigid registration, we propose deep learning-based methods that are trained to find the 3-D position of arbitrarily-oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural ...

为了扩大最先进的受试者间和受试者到模板三维刚性配准的捕获范围并加速其性能，我们提出了基于深度学习的方法，这些方法经过训练，能够根据医学图像的切片或体积，在规范空间中找到任意方向受试者或解剖结构的3D位置。为此，我们提出了回归卷积神经网络...

Accurate segmentation of pelvic organs (i.e., prostate, bladder, and rectum) from CT image is crucial for effective prostate cancer radiotherapy. However, it is a challenging task due to: 1) low soft tissue contrast in CT images and 2) large shape and appearance variations of pelvic organs. In this paper, we employ a two-stage deep learning-based method, with a novel distinctive curve-guided fully...

从CT图像中准确分割盆腔器官（即前列腺、膀胱和直肠）对于有效的前列腺癌放射治疗至关重要。然而，这是一项具有挑战性的任务，原因在于：1) CT图像中软组织对比度低，2) 盆腔器官的形状和外观变化大。本文采用了一种基于两阶段深度学习的方法，并引入了一种新颖的独特曲线引导的全卷积...

Intra-arterial liver cancer therapies, such as trans-arterial chemoembolization, are the preferred therapeutic approaches for advanced hepatocellular carcinoma. However, these palliative techniques are challenging for delivering therapeutic agents selectively in the tumor without real-time 3-D visualization of the catheter within the hepatic arteries. The objective of this paper is to develop and ...

动脉内肝癌治疗，如经动脉化疗栓塞，是晚期肝细胞癌的首选治疗方法。然而，这些姑息性技术在无实时三维导管在肝动脉内可视化的情况下，难以选择性地将治疗剂输送到肿瘤中。本文的目标是开发和评估一种基于光学频域反射计形状传感的动脉内图像引导方法，以提供导管尖端的实时三维定位。

Machine learning algorithms can have difficulties adapting to data from different sources, for example from different imaging modalities. We present and analyze three techniques for unsupervised cross-modality feature learning, using a shared auto-encoder-like convolutional network that learns a common representation from multi-modal data. We investigate a form of feature normalization, a learning...

机器学习算法在适应不同来源的数据（例如来自不同成像模态的数据）时可能会遇到困难。我们提出并分析了三种无监督跨模态特征学习技术，使用一个类似于自动编码器的共享卷积网络，从多模态数据中学习共同表示。我们研究了一种特征归一化形式，一种学习...

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessm...

乳腺癌不同分子分型的分层方法之一是诺丁汉预后指数plus，该方法使用乳腺癌相关生物标志物对制备在组织微阵列（TMA）上的肿瘤组织进行染色。为了确定肿瘤的分子分型，病理学家必须通过显微镜手动标记细胞核活性生物标志物，并使用半定量评估...

Segmentation of suspicious regions (SRs) of a thermal breast image (TBI) is a very significant and challenging problem for the identification of breast cancer. Therefore, in this work, we have proposed an active contour model for the segmentation of the SRs in TBI. The proposed segmentation method combines three significant steps. First, a novel method, called smaller-peaks corresponding to the hi...

热乳腺图像（TBI）中可疑区域（SRs）的分割对于乳腺癌的识别是一个非常重要且具有挑战性的问题。因此，在本工作中，我们提出了一种用于分割TBI中SRs的活动轮廓模型。所提出的分割方法结合了三个重要步骤。首先，一种新方法，称为对应于峰值较小的...

Cerebrovascular diseases, i.e., acute stroke, are a common cause of serious long-term disability. Cerebral perfusion computed tomography (CPCT) can provide rapid, high-resolution, quantitative hemodynamic maps to assess and stratify perfusion in patients with acute stroke symptoms. However, CPCT imaging typically involves a substantial radiation dose due to its repeated scanning protocol. Therefor...

脑血管疾病，如急性卒中，是严重长期残疾的常见原因。脑灌注计算机断层扫描（CPCT）可以提供快速、高分辨率、定量的血流动力学图，用于评估和分层急性卒中症状患者的灌注情况。然而，CPCT成像通常因其重复扫描协议而涉及大量辐射剂量。因此...

X-ray grating interferometry is a powerful emerging tool in biomedical imaging, providing access to three complementary image modalities. In addition to the conventional attenuation modality, interferometry provides a phase modality, which visualizes soft tissue structures, and a dark-field modality, which relates to the number and size of sub-resolution scattering objects. A particularly strong d...

X射线光栅干涉测量是生物医学成像中一种强大的新兴工具，可提供三种互补的图像模态。除了传统的衰减模态外，干涉测量还提供相衬模态（可显示软组织结构）和暗场模态（与亚分辨率散射物体的数量和大小相关）。特别强的...

External beam radiotherapy is extensively used to treat cervical carcinomas. A single planning CT scan enables the calculation of the dose distribution. The treatment is delivered over five weeks. Large per-treatment anatomical variations may hamper the dose delivery, with the potential of an organ-at-risk (OAR) overdose and a tumor underdose. To anticipate these deformations, a recent approach pr...

外照射放疗广泛用于治疗宫颈癌。单个计划CT扫描即可计算剂量分布。治疗持续五周。每次治疗间大的解剖变异可能会妨碍剂量输送，有导致危及器官（OAR）过量照射和肿瘤欠量照射的风险。为预测这些变形，最近的一种方法预...

We present an algorithm for creating high-resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis...

我们提出了一种算法，用于生成与临床脑部MRI扫描（具有大层间距）一致的高分辨率解剖学合理图像。尽管临床图像的大型数据集包含丰富的信息，但采集过程中的时间限制导致稀疏扫描，未能捕获大部分解剖结构。这些特征常常使得计算分析...

As photoacoustic (PA) imaging makes its way into the clinic, the accuracy of PA-based metrics becomes increasingly important. To address this need, a method combining finite-element-based local fluence correction (LFC) with signal-to-noise-ratio (SNR) regularization was developed and validated to accurately estimate oxygen saturation (SO2) in tissue. With data from a Vevo LAZR system, performance ...

随着光声成像进入临床，基于光声的指标的准确性变得越来越重要。为了解决这一需求，结合基于有限元的局部通量校正和信噪比正则化的方法被开发和验证，以准确估计组织中的血氧饱和度。利用Vevo LAZR系统的数据，性能...

Hyper-graph techniques have been widely investigated in computer vision and medical imaging applications, showing superior performance for modeling complex subject-wise relationships and sufficient flexibility to deal with missing data from multi-modal neuroimaging data. Existing hyper-graph methods, however, are inadequate for two reasons. First, representations are generated only from the observ...

超图技术在计算机视觉和医学成像应用中得到了广泛研究，在建模复杂的主体间关系方面表现出优越性能，并且具有足够的灵活性来处理多模态神经影像数据中的缺失数据。然而，现有的超图方法存在两个不足。首先，表示仅从观测数据生成...

The accurate segmentation of the brain surface in post-surgical computed tomography (CT) images is critical for image-guided neurosurgical procedures in epilepsy patients. Following surgical implantation of intracranial electrodes, surgeons require accurate registration of the post-implantation CT images to the pre-implantation functional and structural magnetic resonance imaging to guide surgical...

在癫痫患者术后计算机断层扫描（CT）图像中准确分割脑表面对于图像引导的神经外科手术至关重要。在手术植入颅内电极后，外科医生需要将术后CT图像与术前功能和结构磁共振图像进行精确配准，以指导手术...

In magnetic resonance-based electrical properties tomography (EPT), circularly polarized magnetic field B1 from a transmit radiofrequency (RF) coil is measured and utilized to infer the electrical conductivity and permittivity of biological tissues. Compared with a quadrature RF coil, a multi-channel transmit coil provides a plurality of unique transmit B1 patterns that help to alleviate the under...

在基于磁共振的电特性断层成像（EPT）中，测量来自发射射频线圈的圆极化磁场B1，并利用其推断生物组织的电导率和介电常数。与正交射频线圈相比，多通道发射线圈提供了多种独特的发射B1模式，有助于缓解...

Compared to external device based approaches, a data-driven gating technique in PET imaging is advantageous as it does not require additional hardware or procedure. Currently, data-driven cardiac gating is less studied than respiratory gating. The aim of this paper is to develop a robust data-driven cardiac gating approach for clinical application. First, the central location of the heart is obtai...

与基于外部设备的方法相比，PET成像中的数据驱动门控技术具有优势，因为它不需要额外的硬件或程序。目前，数据驱动心脏门控的研究少于呼吸门控。本文旨在开发一种适用于临床的稳健数据驱动心脏门控方法。首先，获取心脏的中心位置...

In situ visualization of laparoscopic ultrasound in both conventional and robot-assisted laparoscopic surgery requires robust and efficient computation of the pose of the laparoscopic ultrasound probe with respect to the laparoscopic camera. Image-based intrinsic methods of computing this relative pose need to overcome challenges due to irregular illumination, partial feature occlusion, and clutte...

在传统和机器人辅助腹腔镜手术中，腹腔镜超声的现场可视化需要稳健且高效地计算腹腔镜超声探头相对于腹腔镜相机的位姿。基于图像的内在方法计算这种相对位姿需要克服不规则照明、部分特征遮挡和杂波等挑战。