Image translation for medical image generation: Ischemic stroke lesion segmentation

Platscher, Moritz; Zopes, Jonathan; Federau, Christian

doi:10.1016/j.bspc.2021.103283

Cited by 26 publications

(8 citation statements)

References 31 publications

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“…Anomaly detection in BiGAN reduces bad cycle consistency loss due to insufficient sample data information [157]. The approach in [158] generates annotated diffusion-weighted images (DWIs) of brains showing an ischemic stroke (IS). Realistic DWIs are generated from axial slices of these 3D segmentation maps with the help of three generative models: Pix2Pix, SPADE, and cycleGAN.…”

Section: Data Augmentationmentioning

confidence: 99%

A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

et al. 2022

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With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect abnormalities in brain images without an extensive manual feature extraction process. Generative adversarial network (GAN)-synthesized images have many applications in this field besides augmentation, such as image translation, registration, super-resolution, denoising, motion correction, segmentation, reconstruction, and contrast enhancement. The existing literature was reviewed systematically to understand the role of GAN-synthesized dummy images in brain disease diagnosis. Web of Science and Scopus databases were extensively searched to find relevant studies from the last 6 years to write this systematic literature review (SLR). Predefined inclusion and exclusion criteria helped in filtering the search results. Data extraction is based on related research questions (RQ). This SLR identifies various loss functions used in the above applications and software to process brain MRIs. A comparative study of existing evaluation metrics for GAN-synthesized images helps choose the proper metric for an application. GAN-synthesized images will have a crucial role in the clinical sector in the coming years, and this paper gives a baseline for other researchers in the field.

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Section: Data Augmentationmentioning

confidence: 99%

A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

et al. 2022

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“…On the other hand, these images suffer heterogenous in the intensity of each voxel. They are low contrast, low frequency, and wide variety because they belong to different low contrast in medical scans [8][9][10][11][12][13][14]. Furthermore, medical image annotation requires expert doctors and is considered to be a time-consuming task.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, medical image annotation requires expert doctors and is considered to be a time-consuming task. Therefore, the number of existing images for specific diseases is a significant challenge for deep learning models [4,8,11,[15][16][17]. Another challenge in medical-image segmentation, especially for 3D images, is the sturdy imbalance between damaged and normal tissues.…”

Section: Introductionmentioning

confidence: 99%

Ischemic Stroke Lesion Segmentation Using Mutation Model and Generative Adversarial Network

2023

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Ischemic stroke lesion segmentation using different types of images, such as Computed Tomography Perfusion (CTP), is important for medical and Artificial intelligence fields. These images are potential resources to enhance machine learning and deep learning models. However, collecting these types of images is a considerable challenge. Therefore, new augmentation techniques are required to handle the lack of collected images presenting Ischemic strokes. In this paper, the proposed model of mutation model using a distance map is integrated into the generative adversarial network (GAN) to generate a synthetic dataset. The Euclidean distance is used to compute the average distance of each pixel with its neighbor in the right and bottom directions. Then a threshold is used to select the adjacent locations with similar intensities for the mutation process. Furthermore, semi-supervised GAN is enhanced and transformed into supervised GAN, where the segmentation and discriminator are shared the same convolution neural network to reduce the computation process. The mutation and GAN models are trained as an end-to-end model. The results show that the mutation model enhances the dice coefficient of the proposed GAN model by 2.54%. Furthermore, it slightly enhances the recall of the proposed GAN model compared to other GAN models.

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“…Since FLAIR MRI can easily be acquired and has established biomarker relationships with FA and MD metrics, it is a good candidate for image synthesis of MD and FA maps. Synthetic data can augment clinical datasets in segmentation and classification tasks (Conte et al, 2021;Sajjad et al, 2021;Platscher et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Synthesis of diffusion-weighted MRI scalar maps from FLAIR volumes using generative adversarial networks

Chan

Maralani

Moody

et al. 2023

Front. Neuroinform.

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IntroductionAcquisition and pre-processing pipelines for diffusion-weighted imaging (DWI) volumes are resource- and time-consuming. Generating synthetic DWI scalar maps from commonly acquired brain MRI sequences such as fluid-attenuated inversion recovery (FLAIR) could be useful for supplementing datasets. In this work we design and compare GAN-based image translation models for generating DWI scalar maps from FLAIR MRI for the first time.MethodsWe evaluate a pix2pix model, two modified CycleGANs using paired and unpaired data, and a convolutional autoencoder in synthesizing DWI fractional anisotropy (FA) and mean diffusivity (MD) from whole FLAIR volumes. In total, 420 FLAIR and DWI volumes (11,957 images) from multi-center dementia and vascular disease cohorts were used for training/testing. Generated images were evaluated using two groups of metrics: (1) human perception metrics including peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), (2) structural metrics including a newly proposed histogram similarity (Hist-KL) metric and mean squared error (MSE).ResultsPix2pix demonstrated the best performance both quantitatively and qualitatively with mean PSNR, SSIM, and MSE metrics of 23.41 dB, 0.8, 0.004, respectively for MD generation, and 24.05 dB, 0.78, 0.004, respectively for FA generation. The new histogram similarity metric demonstrated sensitivity to differences in fine details between generated and real images with mean pix2pix MD and FA Hist-KL metrics of 11.73 and 3.74, respectively. Detailed analysis of clinically relevant regions of white matter (WM) and gray matter (GM) in the pix2pix images also showed strong significant (p < 0.001) correlations between real and synthetic FA values in both tissue types (R = 0.714 for GM, R = 0.877 for WM).Discussion/conclusionOur results show that pix2pix’s FA and MD models had significantly better structural similarity of tissue structures and fine details than other models, including WM tracts and CSF spaces, between real and generated images. Regional analysis of synthetic volumes showed that synthetic DWI images can not only be used to supplement clinical datasets, but demonstrates potential utility in bypassing or correcting registration in data pre-processing.

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Image translation for medical image generation: Ischemic stroke lesion segmentation

Cited by 26 publications

References 31 publications

A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

Ischemic Stroke Lesion Segmentation Using Mutation Model and Generative Adversarial Network

Synthesis of diffusion-weighted MRI scalar maps from FLAIR volumes using generative adversarial networks

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