Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images

Ahn, Yura; Yoon, Jee Seok; Lee, Seung Soo; Suk, Heung-Il; Son, Jung Hee; Sung, Yu Sub; Lee, Yedaun; Kang, Bo Kyeong; Kim, Ho Sung

doi:10.3348/kjr.2020.0237

Cited by 47 publications

(35 citation statements)

References 33 publications

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“…There are many pre‐processing or post‐processing methods, such as level setting and ML assistance to assist the accuracy of segmentation 24,26 . The accuracy of automatic liver segmentation or volumetry is acceptable with practical clinical application 27 . But the accuracy of tumor segmentation and the derivative radiomics studies should be further improved or validated 28 …”

Section: Artificial Intelligence In Imaging Modalitiesmentioning

confidence: 99%

Artificial intelligence in precision medicine in hepatology

Kao

2021

J of Gastro and Hepatol

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The advancement of investigation tools and electronic health records (EHR) enables a paradigm shift from guideline‐specific therapy toward patient‐specific precision medicine. The multiparametric and large detailed information necessitates novel analyses to explore the insight of diseases and to aid the diagnosis, monitoring, and outcome prediction. Artificial intelligence (AI), machine learning, and deep learning (DL) provide various models of supervised, or unsupervised algorithms, and sophisticated neural networks to generate predictive models more precisely than conventional ones. The data, application tasks, and algorithms are three key components in AI. Various data formats are available in daily clinical practice of hepatology, including radiological imaging, EHR, liver pathology, data from wearable devices, and multi‐omics measurements. The images of abdominal ultrasonography, computed tomography, and magnetic resonance imaging can be used to predict liver fibrosis, cirrhosis, non‐alcoholic fatty liver disease (NAFLD), and differentiation of benign tumors from hepatocellular carcinoma (HCC). Using EHR, the AI algorithms help predict the diagnosis and outcomes of liver cirrhosis, HCC, NAFLD, portal hypertension, varices, liver transplantation, and acute liver failure. AI helps to predict severity and patterns of fibrosis, steatosis, activity of NAFLD, and survival of HCC by using pathological data. Despite of these high potentials of AI application, data preparation, collection, quality, labeling, and sampling biases of data are major concerns. The selection, evaluation, and validation of algorithms, as well as real‐world application of these AI models, are also challenging. Nevertheless, AI opens the new era of precision medicine in hepatology, which will change our future practice.

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Section: Artificial Intelligence In Imaging Modalitiesmentioning

confidence: 99%

Artificial intelligence in precision medicine in hepatology

Kao

2021

J of Gastro and Hepatol

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“…Various CNN architectures have been used depending on deep learning tasks. For a segmentation task, U‐net is most commonly used 22,23 . It consists of multiple contracting and expanding layers that process input images and return a segmentation map with the same resolution of the input image.…”

Section: Deep Learningmentioning

confidence: 99%

“…Although there are several automated or semi‐automated organ segmentation methods based on image processing, these methods are not sufficiently accurate for fully automated organ segmentation without user interaction 24 . Deep learning enables automated abdominal organ segmentation on CT or MR images 22,25,26 . In a recent study, 22 Ahn et al .…”

Section: Deep Learningmentioning

confidence: 99%

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Radiomics and deep learning in liver diseases

Sung

Park

et al. 2021

J of Gastro and Hepatol

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Recently, radiomics and deep learning have gained attention as methods for computerized image analysis. Radiomics and deep learning can perform diagnostic or predictive tasks using high‐dimensional image‐derived features and have the potential to expand the capabilities of liver imaging beyond the scope of traditional visual image analysis. Recent research has demonstrated the potential of these techniques in various fields of liver imaging, including staging of liver fibrosis, prognostication of malignant liver tumors, automated detection and characterization of liver tumors, automated abdominal organ segmentation, and body composition analysis. However, because most of the previous studies were preliminary and focused mainly on technical feasibility, further clinical validation is required for the application of radiomics and deep learning in clinical practice. In this review, we introduce the technical aspects of radiomics and deep learning and summarize the recent studies on the application of these techniques in liver radiology.

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“…Kavur et al tested various AI liver segmentation models on 20 CTs (8 training and 12 test CTs), and found a DSC in the range of 0.79–0.74 in the top four methods. When increasing the number of CTs in the dataset, up to over 800 exams, the DSC exceeded 0.97 [ 21 ]. Interestingly, the DSC was not significantly different across liver conditions (normal liver, fatty liver disease, non-cirrhotic liver disease, liver cirrhosis and post-hepatectomy), so the algorithm developed was robust across possible liver morphology variations.…”

Section: Segmentationmentioning

confidence: 99%

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

Cardobi

Palù

Pedrini

et al. 2021

Cancers

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Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.

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Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images

Cited by 47 publications

References 33 publications

Artificial intelligence in precision medicine in hepatology

Artificial intelligence in precision medicine in hepatology

Radiomics and deep learning in liver diseases

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

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