Automatic liver detection and standardised uptake value evaluation in whole-body Positron Emission Tomography/Computed Tomography scans

Chauvie, Stéphane; Bertone, Elisa; Bergesio, Fabrizio; Terulla, A.; Botto, D.; Cerello, Piergiorgio

doi:10.1016/j.cmpb.2017.12.026

Cited by 5 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Input an SUV lower limit normalized to the individual's measured or predicted lean body mass and an upper limit sufficient to accommodate high activity levels 19,20 . Note: BAT maximal SUVs as high as ~75 g/mL have been reported in previous studies 17 ; thus, 100 g/mL is a reasonable upper limit. 7.…”

Section: Introductionmentioning

confidence: 61%

“…Since BAT has a similar density to WAT and can occur in narrow fascial layers or in small pockets interspersed with WAT 16 , it is difficult to identify using a single, conventional imaging technique. This heterogeneity also makes automatic quantification of BAT more difficult than quantification of homogenous structures such as the liver 17 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Whole Body and Regional Quantification of Active Human Brown Adipose Tissue Using <sup>18</sup>F-FDG PET/CT

Kim

Huang

Fletcher

et al. 2019

JoVE

View full text Add to dashboard Cite

In endothermic animals, brown adipose tissue (BAT) is activated to produce heat for defending body temperature in response to cold. BAT's ability to expend energy has made it a potential target for novel therapies to ameliorate obesity and associated metabolic disorders in humans. Though this tissue has been well studied in small animals, BAT's thermogenic capacity in humans remains largely unknown due to the difficulties of measuring its volume, activity, and distribution. Identifying and quantifying active human BAT is commonly performed using 18 F-Fluorodeoxyglucose ( 18 F-FDG) positron emission tomography and computed tomography (PET/CT) scans following cold-exposure or pharmacological activation. Here we describe a detailed image-analysis approach to quantify total-body human BAT from 18 F-FDG PET/ CT scans using an open-source software. We demonstrate the drawing of user-specified regions of interest to identify metabolically active adipose tissue while avoiding common non-BAT tissues, to measure BAT volume and activity, and to further characterize its anatomical distribution. Although this rigorous approach is time-consuming, we believe it will ultimately provide a foundation to develop future automated BAT quantification algorithms.

show abstract

Section: Introductionmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Whole Body and Regional Quantification of Active Human Brown Adipose Tissue Using <sup>18</sup>F-FDG PET/CT

Kim

Huang

Fletcher

et al. 2019

JoVE

View full text Add to dashboard Cite

show abstract

“…The proposed model, LIDEA, was able to identify the liver and quantify the SUV with 97.3% sensitivity, with a 98.9% correct detection rate when co-registered with CT scans. 14 Oncologic PET scans have also been made available through TCIA, which has accumulated datasets from various imaging modalities. 15 PET tomography imaging datasets are being contributed to TCIA, which should ultimately lead to an increase in studies involving ML in oncology.…”

Section: Machine Learning Pet Analysis: Applications In Oncologymentioning

confidence: 99%

“…Two of note are the Web-Based Imaging Diagnosis by Expert Network (WIDEN) and The Cancer Imaging Archive (TCIA), both of which continue to grow with ongoing contributions which should ultimately lead to an increase in studies involving ML in oncology. [14][15][16] Improving PET Acquisition and Reconstruction With ML…”

Section: Principles Of MLmentioning

confidence: 99%

Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology

2019

View full text Add to dashboard Cite

Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging have emerged. Interest in the use of artificial intelligence in PET imaging for the study of neurodegenerative diseases and oncology stems from the potential for such techniques to streamline decision support for physicians providing early and accurate diagnosis and allowing personalized treatment regimens. In this review, the use of ML to improve PET image acquisition and reconstruction is presented, along with an overview of its applications in the analysis of PET images for the study of Alzheimer's disease and oncology.

show abstract

“…Modern medical imaging plays a vital role in liver tumor diagnosis. Chauvie et al 1 validated a fully automated approach for the liver uptake measurement in whole-body fluorodeoxyglucose positron emission tomography/computed tomography (CT) scans and extracted its average standardized uptake value. Kim et al 2 described the imaging characteristics of primary hepatic angiosarcomas on gadoxetate disodiumenhanced dynamic magnetic resonance imaging and highlighted features that help distinguish angiosarcomas from hemangiomas of similar size.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of liver tumors at different stages using microscopic hyperspectral imaging technology

Wang

2018

J. Biomed. Opt.

View full text Add to dashboard Cite

Liver cancer has one of the highest rates of human morbidity and mortality. However, in terms of pathology, liver cancer is traditionally clinically diagnosed based on observation of microscopic images of pathological liver sections. This paper investigates in vitro samples of rat models of bile duct carcinoma and presents a quantitative analysis method based on microscopic hyperspectral imaging technology to evaluate liver cancers at different stages. The example-based feature extraction method used in this paper mainly includes two algorithms: a morphological watershed algorithm is applied to find object and segment pathological components of pathological liver sections at different stages, and a support vector machine algorithm is implemented for liver tumor classification. Majority/minority analysis is utilized as the postclassification tool to eliminate small plaques from the preliminary classification results. Then, pseudocolor synthesis in RGB color space is used to produce the final results. The experimental results show that this method can effectively calculate the percent tumor areas in liver biopsies at different time points, that is, 3.338%, 11.952%, 15.125%, and 23.375% at 8, 12, 16, and 20 weeks, respectively. Notably, through tracking analysis, the processed results of 8-week images showed the possibility for early diagnosis of the liver tumor.

show abstract

Automatic liver detection and standardised uptake value evaluation in whole-body Positron Emission Tomography/Computed Tomography scans

Abstract: LIDEA proved to be a reliable tool to automatically identify and extract the average SUV of the liver in oncological whole-body PET/CT scans.

Cited by 5 publications

References 17 publications

Whole Body and Regional Quantification of Active Human Brown Adipose Tissue Using <sup>18</sup>F-FDG PET/CT

Whole Body and Regional Quantification of Active Human Brown Adipose Tissue Using <sup>18</sup>F-FDG PET/CT

Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology

Quantitative analysis of liver tumors at different stages using microscopic hyperspectral imaging technology

Contact Info

Product

Resources

About