基于深度学习的多模态医学图像融合方法研究进展

医学图像融合方法可以将有用的信息整合到一张图上，提高单张图像的信息量。对多模态医学图像进行融合时，如何对图像进行有效的变换，提取到不同图像中独有的特征，并施以适当的融合规则是医学图像融合领域研究的重点。近年随着深度学习的快速发展，深度学习被广泛应用于医学图像领域，代替传统方法中的一些人工操作，并在图像表示、图像特征提取以及融合规则的选择方面显示出独特优势。本文针对基于深度学习的医学图像融合进展予以探讨，介绍了卷积神经网络、卷积稀疏表示、深度自编码和深度信念网络这些常用于医学图像融合的框架，对一些应用于融合过程不同步骤的深度学习方法进行分析和总结，最后，分析了当前基于深度学习的融合方法的不足并展望了未来的研究方向。     

基于最大互信息的人脑MR-PET图像配准方法

利用最大互信息法进行多模医学图像配准近来成为医学图像处理领域的热点。MR和PET图像配准对研究神经组织的结构关系和引导神经外科手术有着重要的指导意义。本文描述了一种基于互信息的人脑MR－PET图像配准方法。我们将这种方法应用于图像的几何对准并给出了初步的评估结果。由于不需要对不同成像模式下的图像灰度间的关系作任何假设,最大互信息法是一种稳健性强,可广泛应用于基于体素的多模图像的配准方法。     

基于医学图像的超分辨率重建算法综述

随着临床对医学图像高分辨率的要求,基于低分辨率医学图像的超分辨率重建算法已成为研究热点,该类方法在不需要改进硬件设备的情况下,可以显著提高图像分辨率,因此对其进行综述具有重要意义。针对医学图像领域中特有的超分辨率重建算法,首先分析了该类算法的研究现状,并将其分为三类:基于插值的超分辨率重建、基于重构的超分辨率重建和基于学习的超分辨率重建。同时,基于MR图像、CT图像、超声图像等细分医学图像领域,深入分析了超分辨率重建算法的研究进展,并对不同类型的算法进行了归纳总结和比对分析。其次,对超分辨率重建算法所对应的评价标准也进行了介绍。最后,展望了超分辨率重建技术在医学图像领域的发展趋势。当前应用于医学图像领域的超分辨重建算法已经发展到一定水平,逐步突破基于单一方法的研究形式,通过与机器学习和稀疏表示等理论的深度融合,形成了更高效的算法。     

无监督的磁共振图像重建方法研究进展

近年,深度学习技术在磁共振(magnetic resonance, MR)图像重建领域飞速发展。然而,由于有监督的MR图像重建方法所依赖的高质量配对MR数据难以获取,无监督的MR图像重建方法逐渐成为了研究者们关注的重点,并展现出巨大的应用前景。当前关于此类问题的研究层出不穷,但仍缺乏系统性的归纳和分析。为此,本文综述了无监督MR图像重建方法的研究进展。首先,本文对无监督的MR图像重建方法进行了总结,无监督的MR图像重建方法能够从图像域或K空间域数据学习先验信息,实现在缺少配对数据情况下的MR图像重建;其次,本文根据学习先验信息的作用域的不同,将这些方法分为基于K空间域、基于图像域和基于混合域的无监督MR图像重建方法,并重点对各类方法的算法模型和实现流程进行了详细的介绍。最后,本文对无监督MR图像重建领域的进展和各类方法的特点进行了较为全面的总结,并对未来的发展方向进行了展望,以期为实现无监督MR图像重建提供思路和参考,并促进MR成像的临床应用。     

基于深度学习的医学图像分割研究进展

医学图像分割是医学图像定量分析的关键步骤之一,因此病灶分割对临床诊断有重要意义。针对传统分割方法中存在的过多依赖医学领域的先验知识和人为评估错误等问题,提出了基于深度学习的病灶分割方法。本文总结了卷积神经网络算法应用于医学图像病灶分割的研究进展。首先,论述卷积神经网络的基本结构及其常用架构;其次介绍深度学习在医学图像病灶分割中的应用,其中包括肺结节的检测和分类,脑肿瘤分割和乳腺病灶的分割;最后,分析了目前该研究中存在的优缺点并对深度学习的发展方向进行展望。     

快速自旋回波脉冲序列的设计与实现

快速自旋回波序列可减少磁共振成像的数据扫描时间,提高成像速度,是国内外MR扫描仪必备的快速序列之一。本研究对快速自旋回波序列的设计、编程和实现方法进行了研究,对快速自旋回波序列与自旋回波序列的成像结果进行了比较和分析。该方法为国内MR扫描仪的自主研发提供了重要参考。     

医学图像分割算法的损失函数综述

基于深度学习的医学图像分割方法已经成为了医学图像处理领域的强大工具。由于医学图像的特殊性质，基于深度学习的图像分割算法面临样本不平衡、边缘模糊、假阳性、假阴性等问题，针对这些问题，研究人员大多对网络结构进行改进，而很少从非结构化方面做出改进。损失函数是基于深度学习的分割方法中重要的组成部分，对损失函数的改进可以从根源上提高网络的分割效果，并且损失函数与网络结构无关，可以即插即用地运用在各种网络模型和分割任务中。本文从医学图像分割任务中的困难出发，首先介绍了解决样本不平衡、边缘模糊、假阳性、假阴性问题的损失函数及改进策略；然后对目前损失函数改进过程中所遇到的困难进行分析；最后对未来的研究方向进行了展望。本文将为损失函数的合理选择、改进或创新提供参考，并为损失函数的后续研究指引方向。     

一种基于元学习的小样本核磁共振图像分割方法

将深度学习算法应用于核磁共振（MR）图像分割时，必需以大量经标注后图像作为训练集的数据支撑。然而，MR图像的特殊性导致采集大量的图像数据较困难，制作大量的标注数据成本高。为降低MR图像分割对大量标注数据的依赖，本文提出了一种用于小样本MR图像分割的元U型网络(Meta-UNet)，能够利用少量的图像标注数据完成MR图像分割任务，并获得良好的分割结果。其具体操作为：通过引入空洞卷积对U型网络（U-Net）进行改进，增加网络模型感受野从而提高模型对不同尺度目标的灵敏度；通过引入注意力机制提高模型对不同尺度目标的适应性；通过引入元学习机制，并采用复合损失函数对模型训练进行良好的监督和有效的引导。本文利用提出的Meta-UNet模型，在不同分割任务上进行训练，然后用训练好的模型在全新的分割任务上进行评估，实现了目标图像的高精度分割。新的分割方法比起常用的无监督医学图像配准分割方法——体素变形网络（VoxelMorph）、数据增强医学图像分割方法——转换学习数据增强模型（DataAug）和基于标签转移的医学图像分割方法——标签转移网络（LT-Net）三种模型平均戴斯相似性系数（DSC）有一定提高...     

基于深度学习网络的医学核磁共振成像超分辨率重构实验

基于深度学习网络的医学核磁共振（MR）图像超分辨重建实验研究,提出并构建一个大规模的高质量用于MR图像超分辨的数据集,涵盖了头颅、膝盖、乳房以及头颈4个部位。通过数据质量筛选和不同低分辨率图像生成方式,在原始图像的高分辨率基础下,以×2、×3、×4的下采样尺度,原始MRI图像形成3种不同尺度下的MR图像数据集,同时给出不同部位超分辨难易程度分析。采用7个在自然图像的超分辨率领域中取得最好效果的深度学习网络,将它们迁移到MR图像中,学习低分辨率MR图像到高低分辨MR图像的映射关系,并对比分析这些深度学习网络在自然图像的超分辨效果。通过实验可以看出,深度学习网络在MR图像超分辨取得了比传统算法更好的效果,部分结果不亚于自然图像;不同部位的超分辨效果差异较大,难以以一个深度学习网络使不同部位均具有更好的超分辨效果。深度学习网络在MR图像超分辨将具有重要的应用价值和理论意义。     

脑科学和脑功能MR成像

目的:在对大脑认知功能进行脑功能成像研究之中,随着磁共振成像技术的发展,人们现在可以对脑的认知功能,如视觉、运动、语言和记忆等功能中枢进行成像.本文首先介绍了脑科学的发展历程,并从脑功能MR成像的方法出发,分析了其成像机理,探讨了用脑功能MR成像为手段对脑科学-认知科学进行的方法研究,最后对脑功能MR成像应用于脑科学的研究作了展望.     

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T₁), the spin-spin relaxation time (T₂), and the spin-lattice relaxation in the rotating frame (T_1ρ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T₁-, T₂-, or T_1ρ-weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing “classical” methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.     

Arterial spin labeling MR image denoising and reconstruction using unsupervised deep learning

Arterial spin labeling (ASL) imaging is a powerful magnetic resonance imaging technique that allows to quantitatively measure blood perfusion non-invasively, which has great potential for assessing tissue viability in various clinical settings. However, the clinical applications of ASL are currently limited by its low signal-to-noise ratio (SNR), limited spatial resolution, and long imaging time. In this work, we propose an unsupervised deep learning-based image denoising and reconstruction framework to improve the SNR and accelerate the imaging speed of high resolution ASL imaging. The unique feature of the proposed framework is that it does not require any prior training pairs but only the subject's own anatomical prior, such as T1-weighted images, as network input. The neural network was trained from scratch in the denoising or reconstruction process, with noisy images or sparely sampled k-space data as training labels. Performance of the proposed method was evaluated using in vivo experiment data obtained from 3 healthy subjects on a 3T MR scanner, using ASL images acquired with 44-min acquisition time as the ground truth. Both qualitative and quantitative analyses demonstrate the superior performance of the proposed txtc framework over the reference methods. In summary, our proposed unsupervised deep learning-based denoising and reconstruction framework can improve the image quality and accelerate the imaging speed of ASL imaging.     

Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data

The purpose of this study was to investigate the potential of using clinically provided spine label annotations stored in a single institution image archive as training data for deep learning-based vertebral detection and labeling pipelines. Lumbar and cervical magnetic resonance imaging cases with annotated spine labels were identified and exported from an image archive. Two separate pipelines were configured and trained for lumbar and cervical cases respectively, using the same setup with convolutional neural networks for detection and parts-based graphical models to label the vertebrae. The detection sensitivity, precision and accuracy rates ranged between 99.1–99.8, 99.6–100, and 98.8–99.8% respectively, the average localization error ranges were 1.18–1.24 and 2.38–2.60 mm for cervical and lumbar cases respectively, and with a labeling accuracy of 96.0–97.0%. Failed labeling results typically involved failed S1 detections or missed vertebrae that were not fully visible on the image. These results show that clinically annotated image data from one image archive is sufficient to train a deep learning-based pipeline for accurate detection and labeling of MR images depicting the spine. Further, these results support using deep learning to assist radiologists in their work by providing highly accurate labels that only require rapid confirmation.     

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.     

Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys

Deep learning techniques are being rapidly applied to medical imaging tasks—from organ and lesion segmentation to tissue and tumor classification. These techniques are becoming the leading algorithmic approaches to solve inherently difficult image processing tasks. Currently, the most critical requirement for successful implementation lies in the need for relatively large datasets that can be used for training the deep learning networks. Based on our initial studies of MR imaging examinations of the kidneys of patients affected by polycystic kidney disease (PKD), we have generated a unique database of imaging data and corresponding reference standard segmentations of polycystic kidneys. In the study of PKD, segmentation of the kidneys is needed in order to measure total kidney volume (TKV). Automated methods to segment the kidneys and measure TKV are needed to increase measurement throughput and alleviate the inherent variability of human-derived measurements. We hypothesize that deep learning techniques can be leveraged to perform fast, accurate, reproducible, and fully automated segmentation of polycystic kidneys. Here, we describe a fully automated approach for segmenting PKD kidneys within MR images that simulates a multi-observer approach in order to create an accurate and robust method for the task of segmentation and computation of TKV for PKD patients. A total of 2000 cases were used for training and validation, and 400 cases were used for testing. The multi-observer ensemble method had mean ± SD percent volume difference of 0.68 ± 2.2% compared with the reference standard segmentations. The complete framework performs fully automated segmentation at a level comparable with interobserver variability and could be considered as a replacement for the task of segmentation of PKD kidneys by a human.     

基于深度学习的2D/3D医学图像配准研究

2D/3D配准在临床诊断和手术导航规划中有着广泛的应用,可解决医学图像领域中不同维度图像存在信息缺失的问题,能辅助医生在术中精准定位患者的病灶。常规的2D/3D配准方法主要依赖于图像的灰度进行配准,但非常耗时,不利于临床实时性的需求,并且配准过程中容易陷入局部最优值。提出用深度学习的方法来解决2D/3D医学图像配准问题。采用一个基于深度学习的卷积神经网络,通过网络对数字影像重建技术(DRR)进行训练并自动学习图像特征,预测X光图像所对应的参数,从而实现配准。以人体骨盆的模型骨为实验对象,根据骨盆的CT数据生成36000张DRR图像作为训练集,同时通过C臂采集模型骨的50张X光图像作为验证。结果显示,深度学习算法在相关系数、归一化互信息、欧式距离3个精度评价指标上的测试值分别为0.82±0.07、0.32±0.03、61.56±10.91,而常规2D/3D算法对应的测试值分别为0.79±0.07、0.29±0.03、37.92±7.24,说明深度学习算法的配准精度优于常规2D/3D算法的配准精度,且不存在陷入局部最优值的问题。同时,深度学习的配准时间约为0.03s,远低于常规2D/3D配准的时间,可满足临床对于实时配准的需求,未来将进一步开展临床数据的2D/3D配准研究。     

基于深度学习的肺炎图像目标检测

肺炎是一种严重危害身体健康的疾病,通常使用肺部X光片进行检查。肺炎诊断是肺炎治疗前非常重要的环节,但是由于肺部其他疾病的干扰、医疗数据的爆发式增长以及专业病理医生的缺乏等,导致肺炎的准确诊断较为困难。深度学习能够模仿人脑的机制准确高效地解释医学图像数据,在肺炎图像检测方面获得了广泛应用。构建了3种基于深度学习的图像目标检测模型,单发多框探测器(SSD)、faster-RCNN和faster-RCNN优化模型,对来自Kaggle数据集的26 684张带标签的肺部X光图像进行研究。原始X光图像经预处理后输入3种深度学习模型,分别对单处和两处病灶区域进行目标检测。随机选取500张测试图像,利用损失函数、分类准确率、回归精度和误检病灶数等指标对各模型的性能进行评估。结果表明,faster-RCNN的性能指标优于SSD;Faster-RCNN优化模型的性能指标均优于其他两种模型,其损失函数值小且可快速达到稳定,平均分类准确率为93.7%,平均回归精度为79.8%,且误检病灶数为0。该方法有助于肺炎的准确识别和诊断。     

基于深度学习的低剂量CT图像去噪

目的:提出一种基于深度学习的方法用于低剂量CT（LDCT）图像的噪声去除。方法:首先进行滤波反投影重建,然后利用多尺度并行残差U-net（MPR U-net）的深度学习模型对重建后的LDCT图像进行去噪。实验数据采用LoDoPaB-CT挑战赛的医学CT数据集,其中训练集35 820张图像,验证集3 522张图像,测试集3 553张图像,并采用峰值信噪比（PSNR）与结构相似性系数（SSIM）来评估模型的去噪效果。结果:LDCT图像处理前后PSNR分别为28.80、38.22 dB,SSIM分别为0.786、0.966,平均处理时间为0.03 s。结论:MPR U-net深度学习模型能较好地去除LDCT图像噪声,提升PSNR,保留更多图像细节。