14,755 research outputs found
Lesion Focused Super-Resolution
Super-resolution (SR) for image enhancement has great importance in medical
image applications. Broadly speaking, there are two types of SR, one requires
multiple low resolution (LR) images from different views of the same object to
be reconstructed to the high resolution (HR) output, and the other one relies
on the learning from a large amount of training datasets, i.e., LR-HR pairs. In
real clinical environment, acquiring images from multi-views is expensive and
sometimes infeasible. In this paper, we present a novel Generative Adversarial
Networks (GAN) based learning framework to achieve SR from its LR version. By
performing simulation based studies on the Multimodal Brain Tumor Segmentation
Challenge (BraTS) datasets, we demonstrate the efficacy of our method in
application of brain tumor MRI enhancement. Compared to bilinear interpolation
and other state-of-the-art SR methods, our model is lesion focused, which is
not only resulted in better perceptual image quality without blurring, but also
more efficient and directly benefit for the following clinical tasks, e.g.,
lesion detection and abnormality enhancement. Therefore, we can envisage the
application of our SR method to boost image spatial resolution while
maintaining crucial diagnostic information for further clinical tasks.Comment: 4 pages, 2 figures, 1 table, Accepted as Oral Presentation by the
SPIE Medical Imaging Conference 201
Impact of GAN-based Lesion-Focused Medical Image Super-Resolution on Radiomic Feature Robustness
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceRobust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of
standardized radiomic feature extraction has hampered their clinical use. Since the
radiomic features tend to be affected by low voxel statistics in regions of interest, increasing the sample size would improve their robustness in clinical studies. Therefore,
we propose a Generative Adversarial Network (GAN)-based lesion-focused framework
for Computed Tomography (CT) image Super-Resolution (SR); for the lesion (i.e., cancer) patch-focused training, we incorporate Spatial Pyramid Pooling (SPP) into GANConstrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE).
At 2× SR, the proposed model achieved better perceptual quality with less blurring
than the other considered state-of-the-art SR methods, while producing comparable
results at 4× SR. We also evaluated the robustness of our model’s radiomic feature in
terms of quantization on a different lung cancer CT dataset using Principal Component Analysis (PCA). Intriguingly, the most important radiomic features in our PCAbased analysis were the most robust features extracted on the GAN-super-resolved
images. These achievements pave the way for the application of GAN-based image
Super-Resolution techniques for studies of radiomics for robust biomarker discoveryModelos de machine learning robustos baseados em atributos radiômicos possibilitam
diagnósticos e decisões médicas mais precisas. Infelizmente, por causa da falta de padronização na extração de atributos radiômicos, sua utilização em contextos clÃnicos
tem sido restrita. Considerando que atributos radiômics tendem a ser afetados pelas estatÃtiscas de voxels de baixo volume nas regiões de interesse, o aumento to tamanho da
amostra tem o potencial de melhorar a robustez desses atributos em estudos clÃnicos.
Portanto, esse trabalho propões um framework baseado numa rede neural generativa
(GAN) focada na região de interesse para a super-resolução de imagens de Tomografia
Computadorizada (CT). Para treinar a rede de forma concentrada na lesão (i.e. cancer),
incorporamos a tecnica de Spatial Pyramid Pooling no framework da GAN-CIRCLE.
Nos experimentos de super-resolução 2×, o modelo proposto alcançou melhor qualidade perceptual com menos embaçamento do que outros métodos estado-da-arte
considerados. A robustez dos atributos radiômics das imagens super-resolvidas geradas pelo modelo também foram analizadas em termos de quantização em um banco
de imagens diferente, contendo imagens de tomografia computadorizada de câncer
de pulmão, usando anaálise de componentes principaiss (PCA). Intrigantemente, os
atributos radiômicos mais importantes nessa análise foram também os atributos mais
robustos extraÃdos das imagens super-resolvidas pelo método proposto. Esses resultados abrem caminho para a aplicação de técnicas de super-resolução baseadas em redes
neurais generativas aplicadas a estudos de radômica para a descoberta de biomarcadores robustos.This work was partially supported by The Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre [C9685/A25177], the Wellcome Trust Innovator Award, UK [215733/Z/19/Z] and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [C42780/A27066]. Additional support was also provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). This works was also finantially supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018) and the Slovenian Research Agency (research core funding no. P5-0410).This work was partially supported by The Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre [C9685/A25177], the Wellcome Trust Innovator Award, UK [215733/Z/19/Z] and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [C42780/A27066]. Additional support was also provided by the National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014). This works was also finantially supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018) and the Slovenian Research Agency (research core funding no. P5-0410)
Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
Abstract: Robust machine learning models based on radiomic features might allow for accurate diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized radiomic feature extraction has hampered their clinical use. Since the radiomic features tend to be affected by low voxel statistics in regions of interest, increasing the sample size would improve their robustness in clinical studies. Therefore, we propose a Generative Adversarial Network (GAN)-based lesion-focused framework for Computed Tomography (CT) image Super-Resolution (SR); for the lesion (i.e., cancer) patch-focused training, we incorporate Spatial Pyramid Pooling (SPP) into GAN-Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). At 2× SR, the proposed model achieved better perceptual quality with less blurring than the other considered state-of-the-art SR methods, while producing comparable results at 4× SR. We also evaluated the robustness of our model’s radiomic feature in terms of quantization on a different lung cancer CT dataset using Principal Component Analysis (PCA). Intriguingly, the most important radiomic features in our PCA-based analysis were the most robust features extracted on the GAN-super-resolved images. These achievements pave the way for the application of GAN-based image Super-Resolution techniques for studies of radiomics for robust biomarker discovery
How can we make gan perform better in single medical image super-resolution? A lesion focused multi-scale approach
Single image super-resolution (SISR) is of great importance as a low-level
computer vision task. The fast development of Generative Adversarial Network
(GAN) based deep learning architectures realises an efficient and effective
SISR to boost the spatial resolution of natural images captured by digital
cameras. However, the SISR for medical images is still a very challenging
problem. This is due to (1) compared to natural images, in general, medical
images have lower signal to noise ratios, (2) GAN based models pre-trained on
natural images may synthesise unrealistic patterns in medical images which
could affect the clinical interpretation and diagnosis, and (3) the vanilla GAN
architecture may suffer from unstable training and collapse mode that can also
affect the SISR results. In this paper, we propose a novel lesion focused SR
(LFSR) method, which incorporates GAN to achieve perceptually realistic SISR
results for brain tumour MRI images. More importantly, we test and make
comparison using recently developed GAN variations, e.g., Wasserstein GAN
(WGAN) and WGAN with Gradient Penalty (WGAN-GP), and propose a novel
multi-scale GAN (MS-GAN), to achieve a more stabilised and efficient training
and improved perceptual quality of the super-resolved results. Based on both
quantitative evaluations and our designed mean opinion score, the proposed LFSR
coupled with MS-GAN has performed better in terms of both perceptual quality
and efficiency.Jin Zhu’s PhD research is funded by China Scholarship Council
(grant No.201708060173). Guang Yang is funded by the British
Heart Foundation Project Grant (Project Number: PG/16/78/32402)
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