Confidence-guided Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images in Patients with Post-treatment Malignant Gliomas

08/06/2020 ∙ by Pengfei Guo, et al. ∙ Johns Hopkins Medicine Johns Hopkins University 5

Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images. However, data quantity and quality remain a key determinant of, and a significant limit on, the potential of such applications. In our previous work, we explored synthesis of anatomic and molecular MR image network (SAMR) in patients with post-treatment malignant glioms. Now, we extend it and propose Confidence Guided SAMR (CG-SAMR) that synthesizes data from lesion information to multi-modal anatomic sequences, including T1-weighted (T1w), gadolinium enhanced T1w (Gd-T1w), T2-weighted (T2w), and fluid-attenuated inversion recovery (FLAIR), and the molecular amide proton transfer-weighted (APTw) sequence. We introduce a module which guides the synthesis based on confidence measure about the intermediate results. Furthermore, we extend the proposed architecture for unsupervised synthesis so that unpaired data can be used for training the network. Extensive experiments on real clinical data demonstrate that the proposed model can perform better than the state-of-theart synthesis methods.

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1 Introduction

Glioblastoma (GBM) is the most malignant and frequently occurring type of primary brain tumor in adult. Despite the development of various aggressive treatments, patients with GBM inevitably suffers tumor recurrence with an extremely poor prognosis [4]. The dilemma in the clinical management of post-treatment patients remains precise assessment of the treatment responsiveness. However, it is mostly relied on pathological evaluations of biopsies [5]. Magnetic resonance imaging (MRI) is considered the best non-invasive assessment method of GBM treatment responsiveness [6]. Compared with anatomic MRI, such as T1-weighted (w), gadolinium enhanced w (Gd-w), T2-weighted (w), and fluid-attenuated inversion recovery () images, amide proton transfer-weighted (w) MRI is a novel molecular imaging technique. It has been proved to positively influence the clinical management by different labs across the world [7]

. Recently, convolutional neural network (CNN) based medical image analysis methods have provided exciting solutions in neuro-oncologic community 

[8]. Several studies have demonstrated that CNN-based methods outperform humans on fine-grained classification tasks but require a large amount of accurately annotated data with rich diversity [9]. Compared with demanding large anatomic MRI datasets, it becomes even more impractical when collecting cutting edge MR image data. Furthermore, obtaining aligned lesion annotations on the corresponding co-registrated multi-modal MR images (namely, paired training data) is costly, since expert radiologists are required to label and verify the data. While deploying conventional data augmentations, such as rotation, flipping, random cropping, and distortion, during training partly mitigates such issues, the performance of CNN models is still limited by the diversity of the dataset [10]. In this paper, we address the problem of synthesizing meaningful high quality anatomic w, Gd-w, w, , and molecular w MR images based on the input lesion information.

Goodfellow et al. [11]

proposed the generative adversarial networks (GAN) and first applied to synthesize photo-realistic images. Isola et al.

[2] and Wang et al. [3] further investigated conditional GAN and achieved impressive solution to image-to-image translation problems. Synthesizing realistic MR images is a difficult task since radiographic features dramatically varies on MR images corresponding to underlying diverse pathological changes. Nevertheless, several generative models have been successfully proposed for MRI synthesis. Nguyen et al. [12] and Chartsias et al. [13] proposed CNN-based architectures to synthesize cross-modality MR images. Cordier et al. [14] further used a generative model for multi-modal MR images with brain tumors from a single label map. However, their inputs are conventional MRI modalities, and the diversity of the synthesized images is limited by the training images. Moreover, the method is not yet capable of producing manipulated outputs. Shin et al. [1] adopted Pix2Pix [2] to transfer brain anatomy and lesion segmentation maps to multi-modal MR images with brain tumors. Although, their approach can synthesize realistic brain anatomy for multiple MRI sequences, it does not consider significant differences of radiographic features between anatomic and molecular MRI. Moreover, pathological information are high frequency components and may need extra supervision during synthesis. As a result, their method cannot produce realistic molecular MR images and fails around the lesion region (see Figure 1(b)).

In our previous work, synthesis of anatomic and molecular MR images network (SAMR) [15], a novel generative model was proposed to simultaneously synthesize a diverse set of anatomic and molecular MR images. It takes arbitrarily manipulated lesion masks as input, which is facilitated by brain atlas generated from training data. SAMR [15] is a GAN-based approach, which consists of a stretch-out up-sampling module, a segmentation consistency module, and multi-scale label-wise discriminators. In this paper, we extend SAMR [15]

by incorporating extra supervision on the latent features and their confidence information to further improve the synthetic performance. Intuitively, directly providing the estimated synthesized images (i.e. intermediate results) to the subsequent layers of the network may propagate errors to the final synthesized images. With the confidence map module, the proposed algorithm is capable to measure an uncertainty metric of the intermediate results and block the flow of incorrect estimation. To this end, we formulate a joint task of estimating the confidence score at each pixel location of intermediate results and synthesizing realistic multi-modal MR images. Figure 

1(e) presents sample results from proposed network, where CG-SAMR generates realistic multi-modal brain MR images with more detailed pathological information as compared with Figure 1(b-d). Furthermore, to overcome the insufficiency of paired training data, we modify the network to allow unsupervised training, namely unpaired CG-SAMR (UCG-SAMR). In other words, the proposed unsupervised approach does not require aligned pairs of lesion segmentation maps and multi-modal MR images during training. This is achieved by adding an extra GAN which reverses the synthesis process to a segmentation task. In summary, this paper makes the following contributions:

  • A novel GAN-based model, called CG-SAMR, is proposed to synthesize high quality multi-modal anatomic and molecular MR images with controllable lesion information.

  • A novel stretch-out up-sampling module in the decoder is proposed which performs customized synthesis for images of each MR sequence.

  • Confidence scores of each sequence measured during synthesis are used to guide the subsequent layers for better synthetic performance.

  • Multi-scale label-wise discriminators are developed to provide specific supervision on distinguishing region of interests (ROIs).

  • In order to increase the diversity of the synthesized data, rather than explicitly using white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) masks, we leverage atlas of each sequence to provide brain anatomic information in CG-SAMR.

  • We demonstrate the feasibility of extending the application of CG-SAMR network to unpaired data training.

  • Comparisons have been performed against several recent state-of-the-art paired/unpaired synthesis approaches. Furthermore, an ablation study is conducted to demonstrate the improvements obtained by various components of the proposed method.

Rest of the paper is organized as follows. Section 2 provides a review of some related works. Details of the proposed method are given in Section 3. Implementation details, experimental results, and ablation study are given in Section 4. Finally, Section 5 concludes the paper with a brief discussion and summary.

Figure 2: An overview of the proposed CG-SAMR network. The goal of the CG-SAMR network is to produce realistic multi-model MR images given the corresponding lesion masks and atlases. The orange blocks indicate the encoder part. The green blocks represent the decoder part with stretch-out up-sampling, in which we leverage same latent feature maps to perform customized synthesis for each MRI sequence. The synthesis module produces the intermediate results for each branch of stretch-out up-sampling and is denoted as SM. CM represents the confidence map module that computes confidence maps to guide the subsequent networks. The U-net lesion segmentation module regularize encoder-decoder part to produce lesion regions with correct radiographic features by a lesion shape consistency loss .

2 Related Works

The goal of MR images synthesis is to generate target images with realistic radiographic features [16]. MR image synthesis technically can be achieved by a generative model that translates the source domain to the MR image domain. The source domain usually belongs to noise or different modalities/contrast types (e.g., from CT images to MR images and from w images to w images). In what follows, we review some recent studies on this topic and applications of modeling uncertainty in CNN.

2.1 Conventional Methods

The conventional medical image synthesis methods include intensity-based methods and registration-based methods [17]. Intensity-based methods essentially learn a transformation function mapping source intensities to target intensities. Roy et al. [18] proposed an example-based approach relying on sparse reconstruction from image patches to achieve contrast synthesis and further extended it under the setting of patch-based compressed sensing [19]. Joy et al. [20]

leveraged random forest regression to learn the nonlinear intensity mappings for synthesizing full-head

w images and images. Huang et al. [21] proposed a geometry regularized joint dictionary learning framework to synthesize cross-modality MR images. For registration-based methods, the synthesized images are generated by the registration between a source images and target co-registered images [22]. Cardoso et al. [23] further extended this idea to synthesize expected intensities in an unseen image modality by a template-based multi-modal generative mixture-model.

2.2 CNN-based Methods

With the development of deep learning, CNN-based medical image synthesis methods have shown significant improvements over the conventional methods. Instead of using patch-based methods 

[24, 25], Sevetlidis et al. [26]

introduced a whole image synthesis approach relying on a CNN-based autoencoder architecture. Nguyen

et al. [12] and Chartsias et al. [13] proposed CNN-based architectures integrating intensity features from images to synthesize cross-modality MR images. Various GAN-based methods have also been used for medical image analysis [27, 28]. Shin et al. [1] adopted pix2pix [2] to transfer brain anatomy and lesion segmentation maps to multi-modal MR images with brain tumors. It shows the benefit of using brain anatomy prior, such as WM, GM, CSF masks, to facilitate MR image synthesis.

One major challenge of image synthesis is that paired source/target images are required during training which are expensive to acquire. Recent developments in GAN-based architectures, cycle-consistent adversarial networks (CycleGAN) [29] and unsupervised image-to-image translation networks (UNIT) [30] point to a promising direction for cross-modality biomedical image synthesis using unpaired source/target images). Wolterink et al. [31] leveraged cycle consistency to achieve bidirectional MR/CT images synthesis. Chartsias et al. [32] proposed a two stage framework for MR/CT images synthesis and demonstrated that the synthesized data can further improve the segmentation performance. Zhang et al. [33] and Huo et al. [34] introduced SynSeg-Net to achieve bidirectional synthesis and anatomy segmentation. In their approach, the source domain is the MR images as well as segmentation labels and the target domain is CT images. Inspired by these works, we also add an extra GAN-based network to CG-SAMR and leverage cycle consistency to allow the training using unpaired data.

2.3 Modeling Uncertainty in CNN

Many recent approaches model the uncertainty and use it to benefit the network on different applications. Kendall et al. [35] leveraged the Bayesian deep learning models to demonstrate the benefit of modeling uncertainty on semantic segmentation and depth regression tasks. In [36], Kendall et al. extended the previous work [35]

to multi-task learning by proposing a multi-task loss function maximizing the Gaussian likelihood with homoscedastic uncertainty. Yasarla

et al. [37] and Jose et al. [38] modeled the aleatoric uncertainty as maximum likelihood inference on image restoration and ultrasound image segmentation tasks, respectively. Inspired by these works, we introduce a novel loss function to measure the confidence score of the intermediate synthesis results and guide the subsequent networks of CG-SAMR by the estimated confidence scores.

3 Methodology

Figure 2 gives an overview of the proposed encoder and decoder part in CG-SAMR framework. By incorporating multi-scale label-wise discriminators and shape consistency-based optimization, the generator aims to produce meaningful high-quality anatomical and molecular MR images with diverse and controllable lesion information. While applying 3D convolution operations might reflect the reality of data, the output of the proposed method is multi-modal MRI image slices, since voxel size between anatomical and molecular MRI in axial direction is significantly different and re-sampling to isotropic resolution can severely degrade the image quality. Detailed imaging parameters are given in Section 4.1. In what follows, we describe key parts of the network and training processes using paired and unpaired data.

Figure 3:

(a) Synthesis module. (b) Confidence map module. Here, Conv represents a convolution block that contains a convolutional layer, a batch normalization layer, and a Rectified Linear Units (ReLU) activation.

is the channel-wise concatenation.

3.1 Multi-modal MRI Generation

Our generator architecture is inspired by the models proposed by Johnson et al. [39] and Wang et al. [3]. The generator network, consists of two components (see Fig. 1): an encoder and a decoder with stretch-out up-sampling module. Let the set of multi-model MR images be denoted as and the corresponding set of lesion segmentation maps and anatomic prior as . The generator aims to synthesize multi-modal MR images given input . Unlike many deep learning-based methods that directly synthesize MR images from input, we first estimate the intermediate synthesis results (0.5 scale size of ) and the corresponding confidence map , then use them to guide the synthesis of the final output . The input is passed through the encoder module to get the latent feature maps. Then, the same latent feature maps are passed through each branch of the stretch-out up-sampling block to perform customized synthesis.

The encoder part (orange blocks in Figure 2) consists of a fully-convolutional module with 5 layers and subsequent 3 residual learning blocks (ResBlock) [40]

. We set the kernel size and stride equal to 7 and 1, respectively, for the first layer. For the purpose of down-sampling, instead of using maximum-pooling, the stride of other 4 layers is set equal to 2. Rectified Linear Unit (ReLu) activation and batch normalization are sequentially added after each layer. To learn better transformation functions and representations through a deeper perception, the depth of the encoder network is increased by 3 ResBlocks

[10, 40]. We can observe the significant different radiographic features between anatomic and molecular MR images as shown in Figure 1(a), which vastly increases the difficulty of simultaneous synthesis. To address this issue, the decoder part (green blocks in Figure 2) consists of 3 ResBlocks and a stretch-out up-sampling module that contains 5 same sub-modules designed to utilize the same latent representations from the preceding ResBlock and perform customized synthesis for each MR sequence. Each sub-module contains a symmetric architecture with a fully-convolutional module in the encoder. All convolutional layers are replaced by transposed convolutional layers for up-sampling. The synthesized multi-modal MR images are produced from each sub-model.

3.2 Synthesis and Confidence Map Modules

The synthesis networks are prone to generating incorrect radiographic features at or near the edges, since they are high frequency components. Thus, a special attention in those regions where the network tends to be uncertain can improve the MR image synthesis task. To address this issue, a synthesis module and a confidence map module are added on each branch of the stretch-out up-sampling block (see Synthesis Module (SM) and Confidence Map Module (CM) in Figure 2). Specifically, we estimate the intermediate synthesis results at 0.5 scale size of the final output by SM and measure the confidence map which gives attentions to the the uncertain regions by CM. The confidence score at each pixel is a measurement of certainty about the intermediate results computed at each pixel. Confidence maps produce high confidence values (i.e close to 1) from the regions where the network is certain about the synthesized intensity values, and assign low confidence scores (i.e close to 0) for those pixels where the network is uncertain. To this end, we can block the erroneous regions by combing confidence maps and the intermediate results. Thus, the masked intermediate results is returned to the subsequent networks, which makes the network more attentive in the uncertain regions.

As shown in Figure 3, feature maps at scale 0.5 () are given as input to SM to compute the intermediate results of each MR sequence at scale 0.5. SM is a sequence of four convolutional blocks. Then, we feed the estimated intermediate results and the feature maps as inputs to CM for computing the confidence scores at every pixel. CM is also a sequence of four convolutional blocks. Finally, the confidence-masked intermediate results (i.e. the element-wise multiplication between and ) combining with feature maps at scale are fed back to the network to guide the subsequent layers to produce final output. Inspired by modeling the data dependent aleatoric uncertainty [35, 36], we define the confidence map loss as follows:

(1)

where , are the element-wise multiplication and the channel-wise concatenation, respectively. represents the confidence score at the th row, th column of the confidence map . is intermediate synthesis results produced by the decoder part. In , the first term minimizes the L1 difference between and , and the values of as well. To avoid trivial solution (i.e. ), we introduce the second term as a regularizer. is a constant adjusting the weight of this regularization term . Similar loss has been used for image restoration and ultrasound segmentation tasks in [41, 38]. To the best of our knowledge, our method is the first attempt to introduce this kind of loss in MR synthesis tasks.

Figure 4: An overview of multi-scale label-wise discriminators. ROI masks are produced from reorganized input lesion masks. We denote as the element-wise multiplication operation. GAP is the global average pooling that generates 0.5 scale size of input. is a set of discriminators.

3.3 Multi-scale Label-wise Discriminators

In order to achieve large receptive field in discriminators without introducing deeper networks, we adopt multi-scale PatchGAN discriminators [2], which have identical network architectures but take multi-scale inputs [3]. To distinguish between real and synthesized images, conventional discriminators operate on whole input. However, optimizing generator to produce realistic images in each ROI cannot be guaranteed by discriminating on holistic images, since the difficulty of synthesizing images in different regions is varying. To address this issue, we introduce label-wise discriminators. Based on the radiographic features, original lesion segmentation masks are reorganized into 3 ROIs, including background, normal brain, and lesion. As shown in Figure 4, the input of each discriminator is masked by corresponding ROI. Since the proposed discriminators are in a multi-scale setting, for each ROI there are 2 discriminators that operate on the original and a down-sampled 0.5 scales. Thus, there are in total 6 discriminators for 3 ROIs and we refer to these set of discriminators as . In particular, {,},{,}, and {,} operate on the original and down-sampled versions of background, normal brain, and lesion, respectively. An overview of the proposed discriminators is given in Figure 4. The objective function corresponding to the discriminators is as follows:

(2)

where and are paired input and real multi-modal MR images, respectively. Here, , , and , where denotes element-wise multiplication and corresponds to the ROI mask. For simplicity, we omit the down-sampling operation in this equation.

3.4 Training Using Paired Data

A multi-task loss is designed to train the generator and the discriminators in an adversarial setting. Instead of only using the conventional adversarial loss , we also adopt a feature matching loss [3] to stabilize training, which optimizes generator to match these intermediate representations from the real and the synthesized images in multiple layers of the discriminators. For discriminators, is defined as follows:

(3)

where denotes the th layer of the discriminator , is the total number of layers in and is the number of elements in the th layer. If we perform lesion segmentation on images, it is worth to note that there is a consistent relation between the prediction and the real one serving as input for the generator. Lesion labels are usually occluded with each other and brain anatomic structure, which causes ambiguity for synthesizing realistic MR images. To tackle this problem, we propose a lesion shape consistency loss by adding a U-net [42] segmentation module (see Figure 2) that regularizes the generator to obey this consistency relation. We adopt Generalized Dice Loss (GDL) [43] to measure the difference between the predicted and real segmentation maps and is defined as follows

(4)

where denotes the ground truth and is the segmentation result. and

represent the ground truth and predicted probability maps at each pixel

, respectively. is the total number of pixels. The lesion shape consistency loss is then defined as follows

(5)

where and represent the predicted lesion segmentation probability maps by taking and as inputs in the segmentation module, respectively. denotes the ground truth lesion segmentation map. The final multi-task objective function for training CG-SAMR is defined as

(6)

where , and three parameters that control the importance of each loss.

3.5 Training Using Unpaired Data

Figure 5: The schematic of the proposed method corresponding to training using unpaired data. and are two encoders mapping input to the latent codes. is a decoder with symmetric architecture as encoders mapping the latent codes to domain 1. is a decoder that is used in CG-SAMR mapping the latent codes to multi-modal MR images (domain2). and are two discriminators for domain 1 and domain 2.
Pix2Pix [2] Pix2PixHD [3] Shin et al. [1] SAMR [15] CG-SAMR (our)
Edema Cavity Tumor Lesion Brain Edema Cavity Tumor Lesion Brain Edema Cavity Tumor Lesion Brain Edema Cavity Tumor Lesion Brain Edema Cavity Tumor Lesion Brain
w 50.8 42.1 48.2 48.8 51.0 55.0 42.1 51.2 51.5 52.9 45.2 40.0 42.0 43.9 46.8 65.9 52.7 63.1 63.8 55.1 67.1 51.3 64.3 64.2 56.1
w 54.6 56.2 49.2 53.5 42.4 54.0 53.0 47.8 52.7 44.2 72.6 71.7 68.0 71.8 73.9 73.0 69.0 67.5 72.8 53.4 76.0 67.8 71.1 75.0 57.4
51.7 41.0 44.7 48.5 57.7 47.1 36.3 46.3 44.5 58.8 60.1 41.9 51.9 56.9 65.8 75.4 61.5 68.1 73.1 68.1 78.2 67.4 71.5 76.4 71.6
w 52.1 52.3 42.5 51.2 57.3 50.6 59.3 46.4 50.3 57.8 65.6 55.5 56.3 63.1 70.0 76.7 77.7 71.2 77.3 68.9 81.0 77.7 74.3 80.7 72.5
Gd-w 70.4 57.7 38.1 63.3 58.5 72.3 58.5 37.4 65.0 60.5 74.4 64.8 38.7 67.5 71.4 81.2 67.7 64.2 78.0 69.9 83.1 69.3 62.6 79.1 73.2
Avg. 55.9 49.9 44.5 53.1 53.4 55.8 49.8 45.8 52.8 54.8 63.6 54.8 51.4 60.6 65.6 74.4 65.7 66.8 73.0 63.1 77.1 66.7 68.8 75.1 66.2
Table 1: Quantitative comparison. Quality of the synthesized data under paired data training is measured by pixel accuracy. Lesion indicates the union of edema, cavity, and tumor. Brain represent the holistic brain region. Here, the unit is in percent (%).

Figure 5 shows the schematic of the proposed method corresponding to training using unpaired data. Our framework is based on the proposed CG-SAMR network and an additional GAN: and . Denote the set of lesion segmentation maps and anatomic prior as domain 1 and the set of multi-modal MR images as domain 2. Here, we denote unpaired instances in domain 1 and 2 as and , respectively. In , aims to evaluate whether the translated unpaired instances are realistic. It outputs true for real instances sampled from the domain 1 and false for instances generated by . As shown in Figure 5, can generate two types of instances: (1) instances from the reconstruction stream , and (2) instances from the cross-domain stream . We have similar properties in , but the decoder is replaced by the corresponding decoder part in CG-SAMR. Thus, we can realize confidence-guided customized synthesis for each MR sequence under unpaired data training. The objective functions for reconstruction streams are defined as follows

(7)

where is defined in equation (1) and is a decoder network with the same architecture as used in CG-SAMR. We denote the feature maps used for in as when decoding the latent code obtained by encoding . The objective functions of cross-domain streams can be expressed as follows

(8)

where and are the latent codes, , Simply relaying on the reconstruction stream and adversarial training (i.e. cross-domain streams) cannot guarantee to learn the desired mapping function. To reduce the number of possible mapping functions, we require the learned mapping functions to obey cycle-consistent constraint (i.e. [29]. The objective functions for cycle-reconstruction streams are defined as follows

(9)

The overall objective function used to train the UCG-SAMR in unsupervised setting is defined as follows

(10)
Figure 6: Qualitative comparison of different methods under paired data training. The same lesion mask is used to synthesize images from different methods. (a) Real data (ground truth). (b) Pix2Pix [2]. (c) Pix2PixHD[3]. (d) Shin et al. [1]. (e) CG-SAMR (our). (f) Confidence maps from CG-SAMR. Red boxes indicate the lesion region.
Exp.1: 50% Synthesized+ 50% Real (1080 + 1080)
Dice Score Hausdorff95 Distance
Edema Cavity Tumor Edema Cavity Tumor
Pix2Pix [2] 0.589 0.459 0.562 13.180 21.003 10.139
Pix2PixHD [3] 0.599 0.527 0.571 17.406 8.606 10.369
Shin et al. [1] 0.731 0.688 0.772 7.306 6.290 6.294
SAMR [15] 0.794 0.813 0.821 6.049 1.568 2.293
CG-SAMR (our) 0.804 0.839 0.828 4.166 1.381 1.810
Exp.2: 25% Synthesized+ 75% Real (540 + 1080)
Pix2Pix [2] 0.602 0.502 0.569 10.706 9.431 10.147
Pix2PixHD [3] 0.634 0.514 0.663 17.754 9.512 9.061
Shin et al. [1] 0.673 0.643 0.708 14.835 7.798 6.688
SAMR [15] 0.745 0.780 0.772 8.779 6.757 4.735
CG-SAMR (our) 0.756 0.793 0.773 7.676 6.258 4.325
Exp.3: 0% Synthesized + 100% Real (0 + 1080)
Baseline 0.646 0.613 0.673 8.816 7.856 7.078
Table 2: Quantitative results corresponding to image segmentation when the synthesized data is used for data augmentation. For each experiment, the first row reports the percentage of synthesized/real data for training and the number of instances of synthesized/real data in parentheses. Exp.3 reports the results of baseline trained only by real data.
CycleGAN [29] UNIT [30] UCG-SAMR (our)
Edema Cavity Tumor Lesion Brain Edema Cavity Tumor Lesion Brain Edema Cavity Tumor Lesion Brain
w 51.3 32.8 39.9 47.3 47.3 44.1 33.7 41.3 42.2 42.3 51.5 36.8 44.7 48.2 43.0
w 35.5 23.6 34.2 34.9 53.2 64.5 65.1 64.4 63.1 68.5 66.4 60.5 68.7 65.2 68.0
56.8 33.2 35.2 49.2 60.1 55.3 37.9 49.9 52.3 62.5 65.6 37.1 58.0 60.4 65.9
w 67.0 6.1 54.7 57.1 64.0 63.7 41.4 52.0 58.8 66.6 69.1 48.4 59.5 65.7 68.5
-w 47.5 45.1 22.2 42.5 62.4 65.7 65.3 42.2 62.3 69.7 72.5 65.8 45.3 67.8 69.9
Avg. 51.6 28.2 37.2 46.2 57.4 58.7 48.7 50.0 55.7 61.9 65.0 49.7 55.2 61.5 63.1
Table 3: Quantitative comparison. The quality of synthesized data under unpaired data training is measured by pixel accuracy. Lesion indicates the union of edema, cavity, and tumor. Brain represent the holistic brain region.
Dice Score Hausdorff95 Distance
Edema Cavity Tumor Edema Cavity Tumor
CycleGAN [29] 0.333 0.01 0.073 18.647 30.859 39.611
UNIT [30] 0.527 0.368 0.506 9.008 11.225 10.183
UCG-SAMR (our) 0.558 0.393 0.613 8.321 11.130 7.044
Table 4: Quantitative evaluation of the segmentation performance of different method under unpaired data training.

4 Experiments and Results

In this section, we first discuss the data acquisition and training details. Then, the experimental setup, evaluations of the proposed synthesis methods against a set of recent state-of-the-art approaches, and comprehensive ablation studies are presented.

4.1 Data Acquisition

This study was approved by the Institutional Review Board (IRB) and conducted in accordance with the U.S. Common Rule, and consent form was waved. Patients enrollment criteria are: at least 20 years old; initial diagnosis of pathologically proven primary malignant glioma; status post initial surgery and chemoradiation. There are 90 patients who are involved in this study. MRI scans were acquired by a 3T human MRI scanner (Achieva; Philips Medical Systems) by using a body coil excite and a 32-channel phased-array coil for reception [44]. w, Gd-w, w, , and w MRI sequences were collected for each patient. Imaging parameters for w can be summarized as: field of view (FOV), 212 212 66 ; resolution, 0.82 0.82 4.4 ; size of matrix, 256 256 15. Other anatomic MRI sequences were acquired with Imaging parameters: FOV, 212 212 165 ; resolution, 0.41 0.41 1.1 ; size of matrix, 512 512 150. Co-registration between w and anatomic sequences [45], skull stripping [46], N4-bias field correction [47], and MRI standardization [48] were performed sequentially. After preprocessing, the final volume size of each sequence is 256 256 15. For every collected volume, lesion were manually annotated by an expert neuroradiologist into three labels: edema, cavity and tumor. Then, a multivariate template construction tool [49] was used to create the group average for each sequence (atlas). 1350 instances with the size of 256 256 5 were extracted from volumetric data, where 5 corresponds to five MRI sequences. For every instance, the one corresponding atlas slice and two adjunct (in axial direction) atlas slices were extracted to provide the prior of human brain anatomy in paired data training. The WM, GM, CSF probability masks were also extracted to provide anatomic prior used in the unsupervised case by SPM12 [50]. We split these instances randomly into 1080 (80%) for training and 270 (20%) for testing. Since the data was split on the patient level, training and testing data did not include the instances from the same patient.

4.2 Implementation Detail

The CG-SAMR synthesis model was trained based on the final objective function equation (6) using the Adam optimizer [49]. , and

were set equal to 5, 1 and 1, respectively. Hyperparameters are set as follows: constant learning rate of 2

for the first 250 epochs then linearly decaying to 0; 500 maximum epochs; batch size of 8.

in equation (1) initially was set equal to 0.1. When the mean of scores in confidence maps is greater than 0.7, was set equal to 0.03. Hyperparameters for unpaired data training are set as follows: constant learning rate of 2 for the first 400 epochs then linearly decaying to 0; 800 maximum epochs; batch size of 1. To further evaluating the effectiveness of the synthesized MRI sequences on data augmentation, we leveraged U-net [42] to train lesion segmentation models. U-net [42] was trained by the Adam optimizer [49]. Hyperparameters are set as follows: constant learning rate of 2 for the first 100 epochs then linearly decaying to 0; 200 maximum epochs; batch size of 16. In the segmentation training, all the synthesized data was produced from randomly manipulated lesion masks by CG-SAMR. For evaluation, we always keep 20% of the data unseen for both of the synthesis and segmentation models.

Figure 7: Examples of lesion mask manipulations in CG-SAMR. (a) Real images (ground truth). (b) Synthesized images from the original mask. (c) Synthesized images by increasing tumor size to 100%. (d) Synthesized images by shrinking tumor size to 50%. (e) Synthesized images by replacing lesion from another slice. In lesion masks, gray, green, yellow, and blue represent normal brain, edema, tumor, and cavity, respectively.
Figure 8: Qualitative comparison of segmentation and synthesis performance under unpaired data training. (a) Real data (ground truth). (b) CycleGAN [29]. (c) UNIT [3]. (d) UCG-SAMR (our). (e) Confidence maps from UCG-SAMR. In lesion masks, gray, green, yellow, and blue represent normal brain, edema, tumor, and cavity, respectively.

4.3 Results Corresponding to Supervised Training

We evaluate the performance of our method against the following recent state-of-the-art generic synthesis methods: Pix2Pix [2], Pix2PixHD [3] as well as MRI synthesis methods: Shin et al. [1], and SAMR [15]. We use pixel accuracy to compare the performance of different methods [2, 3, 29]. In particular, we calculate the difference between the synthesized data and the corresponding ground truth data and a pixel translation was counted correct if the difference was within 16 of the ground truth intensity value. Table 1 shows the quantitative performance of different methods in terms of pixel accuracy. As it can be seen from this table, our method clearly outperforms the present state-of-the-art synthesis algorithms. CG-SAMR gains improvement especially at lesion regions. Figure 6 presents the qualitative comparisons of the synthesized multi-modal MRI sequences from four different methods. It can be observed that Pix2Pix [2] and Pix2PixHD [3] fail to synthesize realistic looking human brain MR images. There is either an unreasonable brain ventricle or wrong radiographic features in the lesion region (see Figure 6 (b)(c)). Shin et al. [1] can produce realistic brain anatomic structures for anatomic MRI sequences. However, there is an obvious disparity between the synthesized and real w sequence in both normal brain and lesion region. The boundary of the synthezied lesion is also blurry (see red boxes in see Figure 6 (d)). The proposed method produces more accurate radiographic features of lesions and more diverse anatomic structure based on the human anatomy prior provided by atlas.

To further evaluate the quality of the synthesized MR images, we perform data augmentation by using the synthesized images in training and then perform lesion segmentation. Evaluation metrics in BraTS challenge

[8] (i.e. Dice score, Hausdorff distance (95%)) are used to measure the performance of different methods. The data augmentation by synthesis is evaluated by the improvement for lesion segmentation models. We arbitrarily control lesion information to synthesize different number of data for augmentation. To simulate the piratical usage of data augmentation, we conduct experiments in the manner of utilizing all real data. In each experiment, we vary the percentage of the synthesized data to observe the contribution for data augmentation. Table 2 shows the calculated segmentation performance. Comparing with the baseline experiment that only uses real data, the synthesized data from pix2pix [2] and pix2pixHD [3] degrade the segmentation performance. The performance is improved when the synthesized data from of Shin et al. [1] and SAMR [15] are used for segmentation but the proposed method outperforms the other methods by a large margin. Figure 7 demonstrates the robustness of the proposed model under different lesion mask manipulations (e.g. changing the size of tumor and even reassembling lesion information between lesion masks). As can be seen from this figure, our method is robust to various lesion mask manipulations.

4.4 Results Corresponding to Unsupervised Training

We denote the proposed method under unpaired data training as UCG-SAMR and evaluate its performance against the following recent state-of-the-art unsupervised synthesis methods: CycleGAN [29] and UNIT [30]. Table 3 shows the quantitative synthesis performance of different methods in term of pixel accuracy. As it can be seen from this table, our method outperforms the other state-of-the-art synthesis algorithms. On average, UCG-SAMR gains 5.8% and 15.3% improvement at lesion regions compared to CycleGAN [29] and UNIT [30], respectively. Table 4 shows the comparison of segmentation performance for different methods. We can observe that UCG-SAMR reaches the performance upper bound (i.e. supervised training by real paired data in Table 1 Exp.3). Figure. 8 presents the qualitative comparison of the segmentation and multi-modal MRI synthesis. It can be observed that CycleGAN [29] and UNIT [30] fail to synthesize realistic looking lesions, especially in the w and Gd-w sequences. The proposed method produces more accurate radiographic features for each type of lesion label in both molecular and anatomic sequences. Facilitated by high-quality synthesis, the segmentation network works better than the other models as can be seen from Table 4.

4.5 Ablation Study

Dice Score Hausdorff95 Distance
Edema Cavity Tumor Edema Cavity Tumor
w/o Stretch-out 0.677 0.697 0.679 13.909 11.481 7.123
w/o Multi-label D 0.753 0.797 0.785 7.844 2.570 2.719
w/o Atlas 0.684 0.713 0.705 6.592 5.059 4.002
w/o 0.728 0.795 0.771 8.604 3.024 3.233
w/o 0.794 0.813 0.821 6.049 1.568 2.293
CG-SAMR (proposed) 0.828 0.839 0.828 4.166 1.381 1.810
Table 5: Ablation study of designed modules in data augmentation by synthesis.
w w w Gd-w Avg.
w/o Stretch-out 62.5 66.1 66.2 70.5 72.4 67.5
w/o Label-wise D 63.3 73.8 73.1 74.2 77.1 72.3
w/o Atlas 61.6 66.3 69.2 73.4 73.7 68.8
w/o 63.4 71.7 71.3 75.8 75.8 71.6
w/o 63.8 72.8 73.1 77.3 78.0 73.0
CG-SAMR (proposed) 64.2 75.0 76.4 80.7 79.1 75.1
Table 6: Ablation study of designed modules in term of synthesis quality. The reported value is pixel accuracy in the lesion region as percent (%).

We conduct comprehensive ablation study to separately evaluate the effectiveness of using stretch-out up-sampling module in the decoder network, label-wise discriminators, atlas, lesion shape consistency loss , and confidence map loss in the proposed method. We evaluate each designed module based on two aspects: (1) the effectiveness in data augmentation by the synthesized data, and (2) the contribution on the synthesis quality. For the former, we use the same experimental setting as exp.1 in Table 1. The effectiveness of modules for data augmentation by synthesis is reported in Table 5. Table 6 shows the contribution of designed modules in the MR image synthesis of different sequences. We can observe that two tables show similar trend. Losing the customized reconstruction for each sequence (stretch-out up-sampling module) can severely degrade the synthesis quality. We find that when atlas is not used in our method, it significantly affects the synthesis quality due to the lack of human brain anatomy prior. Moreover, dropping either or label-wise discriminators in the training also reduces the performance, since the shape consistency loss and the specific supervision on ROIs are not used to optimize the generator to produce more realistic images. In addition, dropping the confidence loss can lead to performance degradation, since the supervision on the intermediate results and attention of uncertain regions during synthesis can provide improved results.

5 Conclusion

We proposed an effective generative model, called CG-SAMR, for multi-modal MR images, including anatomic w, Gd-w, w, and , and molecular w. It was shown that the proposed multi-task optimization under adversarial training further improves the synthesis quality in each ROI. The synthesized data could be used for data augmentation, particularly for images with pathological information of gliomas. Moreover, the proposed approach is an automatic, low-cost solution, which is capable to produce high quality data with diverse content that can be used for training of data-driven methods. We further extended CG-SAMR to UCG-SAMR, demonstrating the feasibility of using unpaired data for training.

While our method outperforms state-of-the-art methods to some extent, there are several limitations in our current study. First, all subjects in this study were obtained from a single medical center, so the deep-learning models were not trained and tested on any external data. This leads to the proportional bias in our study without calibration. To make the algorithm more generalizable, our future work will incorporate MRI data from multiple external institutions. Second, our method is geared towards synthesizing 2D MR images. Given the lack of the continuity between adjacent scans and cross-sectional analysis, 2D model compromises the fidelity of MR data. As discussed in section 4.1, along the axial direction, the resolution is 4.4 mm for w images and 1.1 mm for anatomic images. These two non-comparable resolutions limit the application of 3D methods. Moreover, resampling to isotropic for 3D convolution can severely degrade the valuable pathological information in w images. Therefore, in our future work, the proposed method will be extended to 3D synthesis when comparable quality molecular MRI data is available for training.

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