Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment

Blanc-Durand, Paul; Schiratti, Jean-Baptiste; Schutte, Kathryn; Jehanno, Paul; Hérent, Paul; Pigneur, F.; Lucidarme, Olivier; Benaceur, Y.; Sadate, Alexandre; Luciani, Alain; Ernst, Olivier; Rouchaud, A; Crézé, Maud; Dallongeville, Alex; Banaste, Nathan; Cadi, M.; Bousaid, Imad; Lassau, Nathalie; Jégou, Simon

doi:10.1016/j.diii.2020.04.011

Cited by 38 publications

(25 citation statements)

References 15 publications

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“…Recently, various attempts at automated measurements of muscle mass have been made using artificial intelligence (AI). In 2020, Blanc-Durand et al trained a convolutional neural network model using 1025 CT slices to distinguish the muscle areas and reported a dice similarity coefficient (DSC) of 0.97 in 500 test sets that had been separately developed [6]. In the same year, Park et al trained a fully convolutional network model using 883 L3-level CT slices obtained from 467 patients in an attempt to divide the muscle, subcutaneous fat, and visceral fat and reported a DSC of 0.97 in 426 test sets of 308 patients [7].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography

Kim

2021

IJERPH

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The diagnosis of sarcopenia requires accurate muscle quantification. As an alternative to manual muscle mass measurement through computed tomography (CT), artificial intelligence can be leveraged for the automation of these measurements. Although generally difficult to identify with the naked eye, the radiomic features in CT images are informative. In this study, the radiomic features were extracted from L3 CT images of the entire muscle area and partial areas of the erector spinae collected from non-small cell lung carcinoma (NSCLC) patients. The first-order statistics and gray-level co-occurrence, gray-level size zone, gray-level run length, neighboring gray-tone difference, and gray-level dependence matrices were the radiomic features analyzed. The identification performances of the following machine learning models were evaluated: logistic regression, support vector machine (SVM), random forest, and extreme gradient boosting (XGB). Sex, coarseness, skewness, and cluster prominence were selected as the relevant features effectively identifying sarcopenia. The XGB model demonstrated the best performance for the entire muscle, whereas the SVM was the worst-performing model. Overall, the models demonstrated improved performance for the entire muscle compared to the erector spinae. Although further validation is required, the radiomic features presented here could become reliable indicators for quantifying the phenomena observed in the muscles of NSCLC patients, thus facilitating the diagnosis of sarcopenia.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography

Kim

2021

IJERPH

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“…The present review identified a total of 20 articles reporting DICE similarity coefficient scores [ 16 , 19 , 46 – 63 ]. Table 1 lists the included articles with the population characteristics and segmentation approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the evaluation of sarcopenia, abdominal musculature segmentation is accomplished using deep learning with a DICE similarity coefficient of 0.93-0.98 [ 46 , 48 ]. Successful individual segmentation of different muscle groups for SMI are achieved using a DICE similarity coefficient of 0.82-0.95, consisting of psoas major, quadratus lumborum, erector spinae (paraspinal muscle), and abdominal wall muscles (transversus abdominis muscle, internal and external oblique muscle, and rectus abdominis) [ 47 ].…”

Section: Literature Reviewmentioning

confidence: 99%

The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer

et al. 2022

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Sarcopenia is defined as the loss of skeletal muscle mass and muscle function. It is common in patients with malignancies and often associated with adverse clinical outcomes. The presence of sarcopenia in patients with cancer is determined by body composition, and recently, radiologic technology for the accurate estimation of body composition is under development. Artificial intelligence- (AI-) assisted image measurement facilitates the detection of sarcopenia in clinical practice. Sarcopenia is a prognostic factor for patients with cancer, and confirming its presence helps to recognize those patients at the greatest risk, which provides a guide for designing individualized cancer treatments. In this review, we examine the recent literature (2017-2021) on AI-assisted image assessment of body composition and sarcopenia, seeking to synthesize current information on the mechanism and the importance of sarcopenia, its diagnostic image markers, and the interventions for sarcopenia in the medical care of patients with cancer. We concluded that AI-assisted image analysis is a reliable automatic technique for segmentation of abdominal adipose tissue. It has the potential to improve diagnosis of sarcopenia and facilitates identification of oncology patients at the greatest risk, supporting individualized prevention planning and treatment evaluation. The capability of AI approaches in analyzing series of big data and extracting features beyond manual skills would no doubt progressively provide impactful information and greatly refine the standard for assessing sarcopenia risk in patients with cancer.

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“…Additionally, there are a number of other potential pitfalls, especially when using deep learning: 1) the more complex the model topology, the more (labeled) training data is needed to create it, which can be difficult to acquire, 2) monolithic models are harder to verify and validate as intermediate steps are obscured, 3) the model is assumed to “learn” from scratch, ignoring relevant domain (tacit) knowledge, and 4) many medically relevant (and correctly identified) events are rare, creating so‐called unbalanced datasets. The first successful medical applications of ML are primarily situated in medical imaging 51,52 and histopathological screening, 53,54 especially for malignancies. But commonly used approaches, such as convolutional neural networks, have been successfully applied to BioMeT data 55 …”

Section: A Comparison Of Digitally Measured Biomarkers With Laboratormentioning

confidence: 99%

Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience

Godfrey

Vandendriessche

Bakker

et al. 2020

Clinical Translational Sci

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Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well-established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample-level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations. Measure what is measurable and make measurable what is not so. Galileo Galilei (1564-1642).

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Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment

Cited by 38 publications

References 15 publications

Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography

Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography

The Value of Artificial Intelligence-Assisted Imaging in Identifying Diagnostic Markers of Sarcopenia in Patients with Cancer

Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience

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