Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies

Vivanti, Refael; Szeskin, Adi; Lev‐Cohain, Naama; Sosna, Jacob; Joskowicz, Leo

doi:10.1007/s11548-017-1660-z

Cited by 65 publications

(31 citation statements)

References 17 publications

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“…Compared with the visual assessment, this strategy may capture more detailed lesion features and make more accurate diagnosis. According to Vivantil et al by using deep learning models based on longitudinal liver CT studies, new liver tumors could be detected automatically with a true positive rate of 86%, while the stand-alone detection rate was only 72% and this method achieved a precision of 87% and an improvement of 39% over the traditional SVM mode[36]. Some studies[37-39] have also used CNNs based on CT to detect liver tumors automatically, but these machine learning methods may not reliably detect new tumors because of the insufficient representativeness of small new tumors in the training data.…”

Section: Clinical Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence in medical imaging of the liver

Zhou

Wang

et al. 2019

WJG

200

108

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Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.

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Section: Clinical Applicationsmentioning

confidence: 99%

“…A new method[36] to automatically evaluate tumor burden in longitudinal liver CT studies by using a CNN model was developed and the tumor burden volume overlap error was 16%. This work is of great importance with the reason that the tumor burden can be used to evaluate the progression of disease and the response to therapy.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Artificial intelligence in medical imaging of the liver

Zhou

Wang

et al. 2019

WJG

200

108

View full text Add to dashboard Cite

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“…In CT image reconstruction, quantitative measurements of the tissue density are accomplished using the Hounsfield unit (HU) scale, which measures the x-ray absorption characteristics of the tissue and is linearly related to tissue's mass density [39]. At standard pressure and temperature (STP), the radiodensity of distilled water is defined as 0 HU, whereas the radiodensity of air is defined as -1000 HU.…”

Section: A Image Preprocessing Using Intensity Truncationmentioning

confidence: 99%

Deep Learning Initialized and Gradient Enhanced Level-Set Based Segmentation for Liver Tumor From CT Images

Zhang

Jiang

et al. 2020

IEEE Access

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Liver and liver tumor segmentation provides vital biomarkers for surgical planning and hepatic diagnosis. In this paper, we propose and validate a novel level-set method integrating an enhanced edge indicator and an automatically derived initial curve for CT based liver tumor segmentation. At the preprocessing step, the CT image intensity values were truncated to lie in a fixed range to enhance the image contrast surrounding liver and liver tumor. To remove non-liver tissues for subsequent tumor segmentation, liver was firstly segmented using two convolutional neural networks in a coarse-to-fine manner. A 2D slicebased U-net was used to roughly localize the liver and a 3D patch-based fully convolutional network was used to refine the liver segmentation as well as to roughly localize the liver tumor. A novel level-set method was then presented to further refine the tumor segmentation. Specifically, the probabilistic distribution of the liver tumor was estimated using unsupervised fuzzy c-means clustering, which was then utilized to enhance the edge-detector used in level-set. Effectiveness of the proposed pipeline was validated on two publiclyavailable datasets. Experimental results identified the superior segmentation performance of the proposed pipeline over state-of-the-art methods.

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“…Quantifying the tumor load may be useful, particularly for the detection of tumor recurrence in follow-up CT studies. As tumor relapses can be small and go unnoticed, Vivanti et al[ 15 ] described an automated detection method of recurrence, based on the initial appearance of the tumor, its CT behavior, and the quantification of the tumor load at baseline and during the follow-up. The technique had a high rate of true positives in the identification of tumor recurrence, with an accuracy of 86%.…”

Section: Ai In the Diagnosis Of Hccmentioning

confidence: 99%

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

Pérez

Grande

2020

WJG

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Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost all fields. Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma (HCC), as this is a very common tumor, with particular radiological characteristics that allow its diagnosis without the need for a histological study. However, the interpretation and analysis of the resulting images is not always easy, in addition to which the images vary during the course of the disease, and prognosis and treatment response can be conditioned by multiple factors. The vast amount of data available lend themselves to study and analysis by AI in its various branches, such as deep-learning (DL) and machine learning (ML), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns. DL is a more extensive form of learning that attempts to simulate the working of the human brain, using a lot more data and more complex algorithms. This review specifies the type of AI used by the various authors. However, well-designed prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice. In addition, professionals now need to understand the true usefulness of these techniques, as well as their associated strengths and limitations.

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Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies

Cited by 65 publications

References 17 publications

Artificial intelligence in medical imaging of the liver

Artificial intelligence in medical imaging of the liver

Deep Learning Initialized and Gradient Enhanced Level-Set Based Segmentation for Liver Tumor From CT Images

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

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