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Everything you need to know about Computed Tomography (CT) & CT Scanning

Deep Learning: Deep Learning and the Liver Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Deep Learning and the Liver

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  • “Cinematic rendering (CR) is a recently described three-dimensional (3D) rendering technique that generates photorealistic images based on a new lighting model. This review illustrates the potential application of CR in the evaluation of focal liver masses. CR shows promise in improving the visualization of enhancement pattern and internal architecture, local tumor extension, and global disease burden, which may be helpful in focal liver mass characterization and pretreatment planning.”
    Cinematic rendering of focal liver masses
    L.C. Chu, S.P. Rowe, E.K. Fishman
    Diagnostic and Interventional Imaging,Volume 100, Issue 9,2019,Pages 467-476
  • “Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence. This article reviews the current status of artificial intelligence in liver imaging and reviews the opportunities and challenges in clinical implementation.”
    Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman  
    J Comput Assist Tomogr 2021;45: 343–351
  • "Artificial intelligence is commonly performed with supervised learning because of the complexity of medical image analysis. It is provided with annotated “ground truth,” which is used as feedback to improve the algorithm. The degree of data annotation in a detection or segmentation problem can range from labeling subjects as normal versus abnormal, creating an approximate bounding box in the region of the abnormality, to detailed slice-by-slice segmentation of the specific abnormality. Artificial intelligence algorithms depend on large data sets with high-quality images and annotations for training as well as validation, and their performance increases logarithmically with increased training data.”
    Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman  
    J Comput Assist Tomogr 2021;45: 343–351
  • “Radiomics converts imaging data into high-dimensional quanitative features, which can be classified into first order, shape, and texture features. First-order features are derived from histogram distribution of individual voxel signal intensities, which can provide statistics on central tendency, variance, range, and shape of the distribution. Shape features are generated from the 3-dimensional surface mask of the region of interest and can provide measures such as volume, surface area, and sphericity. Texture features, also referred to as second order features, quantify the correlation of signal intensities with respect to surrounding voxels in 3 dimensions. In addition, different types of filters (eg, wavelets, Laplacian of Gaussian) are often applied to the original imaging volume to generate the filtered imaging volume before feature extraction. This process typically generates hundreds of features, and redundant features are eliminated through dimension reduction. Machine learning algorithms, such as random forest and support vector machine, are frequently used to analyze the most relevant features.”
    Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman  
    J Comput Assist Tomogr 2021;45: 343–351
  • “Deep learning-based algorithms have outperformed traditional semiautomatic interactive methods25 and can be significantly faster than manual segmentation.26 In most cases, the deep network is trained to segment the liver in a specific modality (eg, CT or MRI). Wang et al16 trained an algorithm on 1 modality (eg, unenhanced MRI) and was able to adapt it to other modalities (eg, contrast-enhanced CT or MRI) via transfer learning. This type of modality-independent segmentation algorithm may broaden the scope of potential clinical applications.”
    Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman  
    J Comput Assist Tomogr 2021;45: 343–351
  • "Because of black-box nature of the AI models, it can be difficult for the radiologists and clinicians to understand the rationale behind the AI output. This can especially problematic if there is discrepancy between the radiologists' subjective assessment and the AI output. Wang et al sought to make deep learning classification results more “understandable” by training a convolutional neural network with specific training image examples of radiologic features relevant in liver lesion classification. They generated feature maps that ranked the most relevant features used in the lesion classification task. This type of supportive evidence may increase radiologists' confidence in the AI classification.”
    Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman  
    J Comput Assist Tomogr 2021;45: 343–351
  • "Radiomics models have been used to predict recurrence and survival after curative resection, ablation or liver transplantatio8 in patients with HCC. Radiomics models have been able to predict disease recurrence with higher accuracy than traditional clinical models, with the C-index (concordance index measuring goodness of fit for binary outcomes) ranging from 0.47 to 0.82 for radiomics models versus 0.56 to 0.78 for clinical models. The addition of radiomics features to clinical models generally helps to improve risk stratification. It makes intuitive sense that the best prognostic models would incorporate both tumor features (captured by qualitative imaging features and radiomics features) and clinical features that consider background liver disease, patient comorbidities, and performance status.”
    Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman  
    J Comput Assist Tomogr 2021;45: 343–351
  • "Legal ramifications also need to be considered in the clinical implementation of AI. Just like humans, AI is not perfect. The IBM Watson Health's cancer AI algorithm (known as Watson for Oncology) was trained on a small number of synthetic cases with limited input from oncologists. As a result, many output treatment recommendations were erroneous and potentially harmful.93 If a clinician makes a mistake based on flawed AI decision support, who is legally responsible? The black-box nature of many AI algorithms also complicates efforts to tease out the exact cause of any errors. Medical AI systems are too new to have been involved in malpractice lawsuits, and it remains to be seen where the responsibilities lie.”
    Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman  
    J Comput Assist Tomogr 2021;45: 343–351
  • "In this early exploratory phase of applying AI to liver imaging, studies have shown that AI can achieve impressive performance in disease detection, classification, and prognostication under highly controlled experimental settings. These results should be validated in multicenter trials with stringent postmarketing monitoring to ensure safety and efficacy across different practice environments. Artificial intelligence algorithms should be combined to perform comprehensive organ-specific or more general abdominal abnormality detection to be more clinically relevant.”
    Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman  
    J Comput Assist Tomogr 2021;45: 343–351

  • Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman
    J Comput Assist Tomogr 2021;45: 343–351

  • Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman 
    J Comput Assist Tomogr 2021;45: 343–351

  • Current Status of Radiomics and Deep Learning in Liver Imaging  
    Linda C. Chu, Seyoun Park, Satomi Kawamoto, Alan L. Yuille, Ralph H. Hruban, and Elliot K. Fishman 
    J Comput Assist Tomogr 2021;45: 343–351
  • OBJECTIVE To examine whether deep learning recurrent neural network(RNN )models that use raw longitudinal data extracted directly from electronic health records outperform conventional regression models in predicting the risk of developing hepatocellular carcinoma (HCC).
    CONCLUSIONS AND RELEVANCE In this study, deep learning RNN models outperformed conventional LR models, suggesting that RNN models could be used to identify patients with HCV-related cirrhosis with a high risk of developing HCC for risk-based HCC outreach and surveillance strategies.
    Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis
    George N. Ioannou et al.
    JAMA Network Open. 2020;3(9):e2015626. Sept 2020
  • “This study has limitations related to lack of external validation and the computational cost of running the analyses. To reduce computational cost, we only performed optimal search for some of the hyperparameters. Even so, the RNN model outperformed conventional LR models. Health care systems are now investing in the infrastructure to construct some of these complex models. For example, the VHA has collaborated with Google’s DeepMind to develop an RNN model for predicting acute kidney injury using national VHA data. All deep learning neural network models including ours, have limited interpretability due to their black-box nature, which may limit acceptability by clinicians. However, recent innovations allow for interpretable deep learning models by determining the proportion of the prediction attributed to each feature.”
    Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis
    George N. Ioannou et al.
    JAMA Network Open. 2020;3(9):e2015626. Sept 2020
  • "In this study, we demonstrated that RNN models that use raw longitudinal EHR data are superior to conventional LR models in estimating the risk of HCC in patients with HCV-related cirrhosis. RNN models such as ours could have multiple applications in clinical practice, provided they can be incorporated within EHR software systems.”
    Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis
    George N. Ioannou et al.
    JAMA Network Open. 2020;3(9):e2015626. Sept 2020
  • “Morphological features reflect volume, shape, and 3D geometric properties of the segmented ROI/VOI. Examples include diameter, surface area, volume, surface‐to‐volume ratio (an indirect measurement to assess spiculations), compactness, and sphericity. It also describes certain tumor characteristics, including location, vascularization, and presence of necrosis. These features are especially useful in tumor prognostication and diagnosis, including in the differentiation of benign and malignant lesions.”
    State‐of‐the‐art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations
    Santos JM et al.
    Abdominal Radiology 2019 (in press)
  • "First-order statistics features are based on the first‐order histogram that describes distribution of voxel intensities in an image. The most common first‐order features are as fol‐ lows: (a) mean/median, average intensity of the pixels in the ROI/VOI; (b) standard deviation, dispersion of the mean, higher values indicate a wide range of intensity of the pixels in the ROI/VOI; (c) skewness, asymmetry of histogram, with positive values corresponding to a longer right tail of the his‐ togram; (d) kurtosis, magnitude of the histogram, with posi‐ tive values indicating that the curve is taller than a normal distribution; and (e) entropy, irregularity or randomness of the intensities, high values demonstrate a more heterogeneous area.”
    State‐of‐the‐art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations
    Santos JM et al.
    Abdominal Radiology 2019 (in press)
  • "Second-order statistics features consider the inter‐voxel relationships in an image. Using grey level dependence matrices, the second‐order statistical features can be classi‐ fied into three groups: (a) grey level co‐occurrence matrix (GLCM); (b) grey level run length matrix (GLRLM); and (c) grey level size zone matrix (GLSZM). While GLCM considers the incidence of voxels of the same grey value at a predetermined distance along a fixed direction, GLRLM considers the incidence of voxels of the more than one grey alue using a fixed direction and GLSZM uses all matrix directions in the same evaluation.”
    State‐of‐the‐art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations
    Santos JM et al.
    Abdominal Radiology 2019 (in press)
  • “Superior or higher-order statistics features these features have the advantage of considering the relationship with neighboring voxels [36] and are obtained using neighbor‐ hood grayscale difference matrices [32]. In this process, higher‐order statistical methods impose filter grids on the image to extract repetitive or nonrepetitive patterns.”
    State‐of‐the‐art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations
    Santos JM et al.
    Abdominal Radiology 2019 (in press)
  • PURPOSE: Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans.


    CONCLUSIONS: The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.
Automatic 3D liver location and segmentation via convolutional neural network and graph cut.
Lu F et al.
Int J Comput Assist Radiol Surg. 2016 Sep 7. [Epub ahead of print]
  • “Precision medicine relies on an increasing amount of heterogeneous data. Advances in radiation oncology, through the use of CT Scan, dosimetry and imaging performed before each fraction, have generated a considerable flow of data that needs to be integrated. In the same time, Electronic Health Records now provide phenotypic profiles of large cohorts of patients that could be correlated to this information. In this review, we describe methods that could be used to create integrative predictive models in radiation oncology. Potential uses of machine learning methods such as support vector machine, artificial neural networks, and deep learning are also discussed.”

    
Big Data and machine learning in radiation oncology: State of the art and future prospects.
Bibault JE, Giraud P, Burgun A
Cancer Lett. 2016 May 27. pii: S0304-3835(16)30346-9. doi: 10.1016/j.canlet.2016.05.033. [Epub ahead of print]
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