深度学习反卷积神经网络在医学图像高分辨率重建中的应用

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"学习反卷积深度神经网络进行高分辨率医学图像重建" 这篇研究论文探讨了如何利用反卷积深度神经网络（Deconvolutional Deep Neural Networks, DDNNs）来实现高分辨率医学图像的重建。在医学成像领域，高质量的图像对于疾病的诊断和治疗至关重要。然而，由于设备限制或为了减少辐射剂量，临床实践中经常获得的是低分辨率的图像。超级分辨率重建技术（Super-resolution Reconstruction）旨在通过算法将这些低分辨率图像提升至高分辨率，从而提供更多的细节信息。文章中提到，反卷积层是深度学习网络中用于上采样和图像恢复的关键组成部分。它们通过学习反卷积权重来逆转卷积操作，进而增加图像的分辨率。研究人员采用了预激活线性单元（Parametric Rectified Linear Unit, PReLU）作为激活函数，以改善网络的训练性能和图像恢复质量。PReLU相比传统的ReLU函数具有更平滑的特性，能有效解决梯度消失问题，尤其是在处理负值输入时。此外，论文可能涉及了以下几点内容： 1. 数据集：研究可能使用了各种医学图像数据集，如CT、MRI等，用于训练和测试深度学习模型。 2. 网络架构：DDNN的具体结构可能包括多个卷积层、反卷积层以及正则化策略，以防止过拟合并提高泛化能力。 3. 训练策略：可能包含了批量归一化、动量优化器等训练技巧，以加速收敛并提升模型的性能。 4. 评估指标：研究可能使用了峰值信噪比（PSNR）、结构相似性指数（SSIM）等指标来衡量高分辨率图像重建的质量。 5. 应用场景：除了基础的图像分辨率提升，该技术可能还讨论了其在病灶检测、病程跟踪、图像分析等临床应用中的潜力。论文的接收日期为2018年4月10日，经过修订后于8月8日被接受，并于8月11日在线发布。根据给出的MSC分类号，这是一篇跨领域的研究，结合了数学、计算机科学和医学物理学的知识。这篇研究论文展示了反卷积深度神经网络在医学图像处理中的创新应用，对于提高医学成像的精度和临床价值具有重要意义。

144 H. Liu et al. / Information Sciences 468 (2018) 142–154

Fig. 1. Architecture of SRCNN and the proposed SR-DCNN for SR image.

ages, but also in medical image applications, such as ROI segmentation [14] , lesion detection [44] and tomographic imaging

[35,40] , where deeper networks have achieved great success [28] . Although these types of networks require a large amount

of data and a correspondingly large training time, the test stage is relatively short and good results can be obtained.

In this paper, a single-channel CNN was applied to the SR reconstruction of grayscale medical images, which was im-

proved from the SRCNN network [2] . One big difference is that a deconvolution layer is adopted as the ﬁrst layer in the

model we proposed instead of the bicubic interpolation in SRCNN, which was inspired by the success of the deconvolution

operation in image saliency detection [42] and semantic segmentation [25] . This network is therefore termed SR Deconvo-

lution CNN (SR-DCNN), which facilitates SR reconstruction by directly learning the mapping between LR and HR images. In

deconvolution, the ﬁlter parameters can be updated as the number of iterations increases. Under the evidence of laboratory

data, the four-layer lightweight network constructed in this paper achieved better results than SRCNN. In our network, the

bicubic interpolation was used to generate the network input as a preprocessing step, which provides an effective solution to

the inconsistent mapping problem of input image existed in SRCNN and sparse encoding algorithms. Instead of the rectiﬁed

linear unit (ReLU), the parametric rectiﬁed linear unit (PReLU) [9] was introduced as the activation function in each layer.

In this approach, the coeﬃcient of the negative part was computed to avoid the problem of “dead characteristic” problem

caused by zero parameters of traditional ReLU.

The remainder of this paper is organized as follows. Section 2 describes the architecture of the proposed SR-DCNN in-

cluding related forward and back propagation processes. In Section 3 , the objective functions is deﬁned, and the performance

of proposed method is evaluated and compared to the state-of-the-art methods. In addition, a description of the discussed

inﬂuence of the hyper parameters on achieving an optimal tradeoff between the network performance and computational

eﬃciency is presented. Finally, the conclusions drawn from this research are summarized in Section 4 .

2. Method

The architecture of the proposed SR-DCNN network is shown and compared with that of SRCNN in Fig. 1 . The classical

SRCNN employs bicubic interpolation to amplify the image, and the convolutional network consists of three-layers, each of

which is followed by a ReLU function. In contrast, the proposed SR-DCNN contains one deconvolutional layer and multiple

convolutional layers, and each layer is followed by a PReLU function. The workﬂow begins by down-sampling an LR medical

image from the original HR image to serve as the input image. The network construction details are as follows.

2.1. Forward propagation

The purpose of the proposed method is to learn a mapping F to recover the HR image from an LR image Y such that F ( Y )

is as similar as possible to the original HR image X . An example patch extraction diagram is shown in Fig. 2 . The workﬂow

is as follows.

A single HR image is down-sampled via bicubic interpolation. Note that this is the only preprocessing step applied. The

result of this step is an LR image with a size of 1/ d

of the original HR image. The original image is denoted as X , and the

down-sampled image is denoted as Y . The positional alignment between the LR image and the original HR image is obtained

via bicubic interpolation. The pixel value of the down-sampled image is calculated from the nearest 16 neighbor pixels in

the corresponding position of the original image.

Then, each LR medical image Y is divided into blocks having a size of n × n with a stride of s . Similarly, the corresponding

HR image X is divided into blocks having a size of m × m with a stride of 2 s, which is used as label of Y . Finally, the LR and

HR image blocks have a one to one correspondence.

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