Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization

Onofrey, John A.; Casetti-Dinescu, Dana I.; Lauritzen, Andreas D.; Sarkar, S; Venkataraman, Rajesh; Fan, Richard E.; Sonn, Geoffrey A.; Sprenkle, Preston; Staib, Lawrence H.; Papademetris, Xenophon

doi:10.1109/isbi.2019.8759295

Cited by 44 publications

(33 citation statements)

References 11 publications

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“…Following this, signal intensities within each image are linearly transformed (normalized) to either the [0, 1] or [0, 255] range. There are also many other approaches to normalization-Gaussian and Z-score normalization are two common alternatives [ 93 , 94 ]. The normalization process will impact the values of the different radiomic features, influencing the information represented by each image and potentially interobserver reliability [ 95 ].…”

Section: Radiomics Pipeline For Predicting Tumor Gradementioning

confidence: 99%

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Chaddad

Kucharczyk

Cheddad

et al. 2021

Cancers

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The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.

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Section: Radiomics Pipeline For Predicting Tumor Gradementioning

confidence: 99%

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Chaddad

Kucharczyk

Cheddad

et al. 2021

Cancers

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“…However, this simple strategy does not take into account the global statistics of the dataset. Other pre-processing approaches, such as histogram equalization (Onofrey et al, 2019) and bias field correction (Birenbaum and Greenspan, 2017;Baid et al, 2018;Feng et al, 2019), are also commonly used to mitigate the problem of intensity inhomogeneity in images. For instance, Onofrey et al (2019) evaluate the benefit of using different normalization techniques to multi-site prostate MRI before applying deep learning-based segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…This leads to sub-optimal performance when evaluating on different sets of medical images (Mårtensson et al, 2020). A common strategy to address this problem is to normalize images in a pre-processing step, for instance, so that their intensities fall in the same range or have the same global mean (Onofrey et al, 2019). However, this naive approach is generally insufficient for tasks such as segmentation since it does not consider the intensity distribution of individual regions in the image.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Normalization for Multi Domain Image Segmentation

Delisle

Anctil-Robitaille

Desrosiers

et al. 2020

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)

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Image normalization is a building block in medical image analysis. Conventional approaches are customarily utilized on a perdataset basis. This strategy, however, prevents the current normalization algorithms from fully exploiting the complex joint information available across multiple datasets. Consequently, ignoring such joint information has a direct impact on the performance of segmentation algorithms. This paper proposes to revisit the conventional image normalization approach by instead learning a common normalizing function across multiple datasets. Jointly normalizing multiple datasets is shown to yield consistent normalized images as well as an improved image segmentation. To do so, a fully automated adversarial and task-driven normalization approach is employed as it facilitates the training of realistic and interpretable images while keeping performance on-par with the state-of-theart. The adversarial training of our network aims at finding the optimal transfer function to improve both the segmentation accuracy and the generation of realistic images. We evaluated the performance of our normalizer on both infant and adult brains images from the iSEG, MRBrainS and ABIDE datasets. Results reveal the potential of our normalization approach for segmentation, with Dice improvements of up to 57.5% over our baseline. Our method can also enhance data availability by increasing the number of samples available when learning from multiple imaging domains.

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“…Image normalization is a key pre-processing step for deep learning algorithms [10]. Unlike CT images, in which the image intensity (Hounsfield Unit, HU) represents attenuation as relative to water and the intensity range is consistent across all the patients, the PET image intensity represents the tracer uptake level.…”

Section: Image Preprocessingmentioning

confidence: 99%

A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation Using Deep Learning

Shi

Onofrey

Revilla

et al. 2019

Lecture Notes in Computer Science

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In PET/CT imaging, CT is used for PET attenuation correction (AC). Mismatch between CT and PET due to patient body motion results in AC artifacts. In addition, artifact caused by metal, beam-hardening and count-starving in CT itself also introduces inaccurate AC for PET. Maximum likelihood reconstruction of activity and attenuation (MLAA) was proposed to solve those issues by simultaneously reconstructing tracer activity (λ-MLAA) and attenuation map (μ-MLAA) based on the PET raw data only. However, μ-MLAA suffers from high noise and λ-MLAA suffers from large bias as compared to the reconstruction using the CT-based attenuation map (μ-CT). Recently, a convolutional neural network (CNN) was applied to predict the CT attenuation map (μ-CNN) from λ-MLAA and μ-MLAA, in which an image-domain loss (IM-loss) function between the μ-CNN and the ground truth μ-CT was used. However, IM-loss does not directly measure the AC errors according to the PET attenuation physics, where the line-integral projection of the attenuation map (μ) along the path of the two annihilation events, instead of the μ itself, is used for AC. Therefore, a network trained with the IM-loss may yield suboptimal performance in the μ generation. Here, we propose a novel line-integral projection loss (LIP-loss) function that incorporates the PET attenuation physics for μ generation. Eighty training and twenty testing datasets of whole-body 18 F-FDG PET and paired ground truth μ-CT were used. Quantitative evaluations showed that the model trained with the additional LIP-loss was able to significantly outperform the model trained solely based on the IM-loss function.

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Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization

Cited by 44 publications

References 11 publications

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Adversarial Normalization for Multi Domain Image Segmentation

A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation Using Deep Learning

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