Large‑scale analysis of iliopsoas muscle volumes in the UK Biobank

Psoas muscle measurements are frequently used as markers of sarcopenia and predictors of health. Manually measured cross-sectional areas are most commonly used, but there is a lack of consistency regarding the position of the measurement and manual annotations are not practical for large population studies. We have developed a fully automated method to measure iliopsoas muscle volume (comprised of the psoas and iliacus muscles) using a convolutional neural network. Magnetic resonance images were obtained from the UK Biobank for 5000 participants, balanced for age, gender and BMI. Ninety manual annotations were available for model training and validation. The model showed excellent performance against out-of-sample data (average dice score coefficient of 0.9046 ± 0.0058 for six-fold cross-validation). Iliopsoas muscle volumes were successfully measured in all 5000 participants. Iliopsoas volume was greater in male compared with female subjects. There was a small but significant asymmetry between left and right iliopsoas muscle volumes. We also found that iliopsoas volume was significantly related to height, BMI and age, and that there was an acceleration in muscle volume decrease in men with age. Our method provides a robust technique for measuring iliopsoas muscle volume that can be applied to large cohorts

Analyse der Körperzusammensetzung: Messung der Skelettmuskulatur mit Computertomographie und Implikationen für die Patientenversorgung

Objective: This thesis aims to evaluate the relationship between the skeletal muscle index derived from computed tomography (CT) images and patient outcomes, as well as its implications for patient care. This goal was pursued in five individual studies: Studies A and B evaluated the relationship between the lumbar skeletal muscle index (L3SMI) and patient outcomes in the intensive care unit (ICU) and oncology setting, respectively. Studies C and D evaluated the effect of CT acquisition parameters on body composition measures. Study E proposed a novel technique to automate the segmentation of skeletal muscle using a fully automated deep learning system. Material and methods: In total, 1328 axial CT images were included in the five studies. Patients in studies A and B were part of the clinical trials NCT01967056 and NCT01401907 at Massachusetts General Hospital (MGH), respectively. Body composition indices were computed using semi-automated segmentation. Multivariable regression models with a priori defined covariates were used to analyze clinical outcomes. To evaluate whether CT acquisition parameters influence segmentation, the Bland-Altman approach was used. In study E, a fully convolutional neural network was implemented for deep learning-based automatic segmentation. Results: Study A found lower L3SMI to be a predictor of increased mortality within 30 days of extubation (p = 0.033), increased rate of pneumonia within 30 days of extubation (p = 0.002), increased adverse discharge disposition (p = 0.044), longer hospital stays post-extubation (p = 0.048), and higher total hospital costs (p = 0.043). In study B, low L3SMI was associated with worse quality of life (p = 0.048) and increased depression symptoms (p = 0.005). Threshold-based segmentation of skeletal muscle in study C and adipose tissue compartments in study D were significantly affected by CT acquisition parameters. The proposed deep learning system in study E provided automatic segmentation of skeletal muscle cross-sectional area and achieved a high congruence to segmentations performed by domain experts (average Dice coefficient of 0.93). Conclusion: L3SMI is a useful tool for the assessment of muscle mass in clinical practice. In critically ill patients, L3SMI can provide clinically useful information about patient outcomes at the time of extubation. Patients with advanced cancer who suffered from low muscle mass reported worse quality of life and increased depression symptoms. This highlights the clinical relevance of addressing muscle loss early on as part of a multimodal treatment plan. Importantly, indices utilized in body composition analysis are significantly affected by CT acquisition parameters. These effects should be considered when body composition analysis is used in clinical practice or research studies. Finally, our fully automated deep learning system enabled instantaneous segmentation of skeletal muscle.Zielsetzung: Das Ziel der vorliegenden Dissertation war es, den Einfluss des auf CT-Bildern berechneten Skelettmuskelindexes auf klinische Ergebnisse von Patienten und die daraus resultierenden Implikationen für die Patientenversorgung zu evaluieren. Dieses Ziel wurde in fünf Einzelstudien verfolgt: In den Studien A und B wurde der Einfluss des lumbalen Skelettmuskelindex (L3SMI) auf klinische Endpunkte von Patienten auf der Intensivstation sowie in der Onkologie untersucht. Die Studien C und D evaluierten die Auswirkungen von CT-Akquisitionsparametern auf Indizes der Körperzusammensetzung. Studie E stellte eine neuartige Technik der automatisierten Segmentierung von Skelettmuskulatur vor, die durch maschinelles Lernen ermöglicht wurde. Material und Methoden: Insgesamt wurden 1328 axiale CT-Bilder in die fünf Studien eingeschlossen. Die Patienten der Studien A und B waren Teilnehmer der klinischen Studien NCT01967056 und NCT01401907 am Massachusetts General Hospital. Die Indizes der Körperzusammensetzung wurden mithilfe halbautomatischer Segmentierung berechnet. Die klinischen Endpunkte wurden in multivariablen Regressionsmodellen mit a priori definierten Kovariaten analysiert. Um zu evaluieren, ob CT-Akquisitionsparameter die Segmentierung beeinflussen, wurde der Bland-Altman-Ansatz verwendet. In Studie E wurden ein künstliches neuronales Netzwerk sowie maschinelles Lernen für die automatische Segmentierung eingesetzt. Ergebnisse: In Studie A war ein niedriger L3SMI ein Prädiktor für eine höhere Mortalität (p = 0.033) und Pneumonierate (p = 0.002) innerhalb von 30 Tagen nach der Extubation sowie für mehr ungünstige Entlassungen (p = 0.044) und höhere Behandlungskosten für den gesamten Krankenhausaufenthalt (p = 0.043). Ein niedriger L3SMI war in Studie B mit einer schlechteren Lebensqualität (p = 0.048) und stärkeren depressiven Symptomen (p = 0.005) assoziiert. Die schwellenwertbasierte Segmentierung der Skelettmuskulatur in Studie C und der Fettgewebekompartimente in Studie D wurde durch CT-Akquisitionsparameter signifikant beeinflusst. Das in Studie E vorgestellte vollautomatische Segmentierungssystem erreichte eine hohe Übereinstimmung mit den durch Experten erstellten Segmentationen (durchschnittlicher Dice-Koeffizient von 0.93). Fazit: Der L3SMI ist ein Werkzeug zur Beurteilung von Muskelmasse. Bei Intensivpatienten kann L3SMI zum Zeitpunkt der Extubation nützliche klinische Informationen liefern. Patienten mit fortgeschrittener Krebserkrankung, die zudem eine geringere Muskelmasse hatten, berichteten über eine schlechtere Lebensqualität und stärkere depressive Symptome. Dies unterstreicht die Notwendigkeit, die Muskulatur frühzeitig als Teil eines multimodalen Behandlungskonzeptes zu adressieren. Die Indizes der Körperzusammensetzung werden signifikant von CT-Akquisitionsparametern beeinflusst. Darüber hinaus ermöglichte unser vollautomatisiertes System dank maschinellen Lernens die verzögerungsfreie Segmentierung von Skelettmuskulatur

Association between CT-Based Preoperative Sarcopenia and Outcomes in Patients That Underwent Liver Resections.

This retrospective observational study aimed to evaluate whether preoperative sarcopenia, assessed by CT imaging, was associated with postoperative clinical outcomes and overall survival in patients that underwent liver resections. Patients operated on between January 2014 and February 2020 were included. The skeletal muscle index (SMI) was measured at the level of the third lumbar vertebra on preoperative CT scans. Preoperative sarcopenia was defined based on pre-established SMI cut-off values. The outcomes were postoperative morbidity, length of hospital stay (LOS), and overall survival. Among 355 patients, 212 (59.7%) had preoperative sarcopenia. Patients with sarcopenia were significantly older (63.5 years) and had significantly lower BMIs (23.9 kg/m <sup>2</sup> ) than patients without sarcopenia (59.3 years, p < 0.01, and 27.7 kg/m <sup>2</sup> , p < 0.01, respectively). There was no difference in LOS (8 vs. 8 days, p = 0.75), and the major complication rates were comparable between the two groups (11.2% vs. 11.3%, p = 1.00). The median overall survival times were comparable between patients with sarcopenia and those without sarcopenia (15 vs. 16 months, p = 0.87). Based on CT assessment alone, preoperative sarcopenia appeared to have no impact on postoperative clinical outcomes or overall survival in patients that underwent liver resections. Future efforts should also consider muscle strength and physical performance, in addition to imaging, for preoperative risk stratification

Cultivate Quantitative Magnetic Resonance Imaging Methods to Measure Markers of Health and Translate to Large Scale Cohort Studies

Magnetic Resonance Imaging (MRI) is an indispensable tool in healthcare and research, with a growing demand for its services. The appeal of MRI stems from its non-ionizing radiation nature, ability to generate high-resolution images of internal organs and structures without invasive procedures, and capacity to provide quantitative assessments of tissue properties such as ectopic fat, body composition, and organ volume. All without long term side effects. Nine published papers are submitted which show the cultivation of quantitative measures of ectopic fat within the liver and pancreas using MRI, and the process of validating whole-body composition and organ volume measurements. All these techniques have been translated into large-scale studies to improve health measurements in large population cohorts. Translating this work into large-scale studies, including the use of artificial intelligence, is included. Additionally, an evaluation accompanies these published studies, appraising the evolution of these quantitative MRI techniques from the conception to their application in large cohort studies. Finally, this appraisal provides a summary of future work on crowdsourcing of ground truth training data to facilitate its use in wider applications of artificial intelligence.In conclusion, this body of work presents a portfolio of evidence to fulfil the requirements of a PhD by published works at the University of Salford

Body composition assessment: comparison of quantitative values between magnetic resonance imaging and computed tomography.

Background The primary objective of this study was to compare measurements of skeletal muscle index (SMI), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) at the level of L3, on subjects who underwent computed tomography (CT) and magnetic resonance imaging (MRI) examinations within a three-month period. The secondary objective was to compare the automatic and semi-automatic quantifications of the same values for CT images. Methods Among subjects who underwent CT and MRI at our Institution between 2011 and 2020, exclusion criteria were: presence of extensive artifacts; images not including the whole waist circumference; CT acquired with low-dose technique and lack of non-contrast images. A set of three axial images (CT, MRI T1-weighted and T2-weighted) were used to extract the following measurements with semi-automatic segmentations: SMI [calculated normalizing skeletal muscle area (SMA) by the square height], SAT, VAT. For the CT images only, the same values were also calculated by using automatic segmentation. Statistical analysis was performed comparing quantitative MRI and CT measurements by Pearson correlation analysis and by Bland-Altman agreement analysis. Results A total of 123 patients were included. By performing linear regression analysis, CT and MRI measurements of SMI showed a high correlation (r2=0.81 for T1, r2=0.89 for T2), with a mean logarithmic difference between CT and MRI quantitative values of 0.041 for T1-weighted and 0.072 for T2-weighted images. CT and MRI measurements of SAT showed high correlation (r2=0.81 for T1; r2=0.81 for T2), with a mean logarithmic difference between CT and MRI values of 0.0174 for T1-weighted and 0.201 for T2-weighted images. CT and MRI measurements of VAT showed high correlation (r2=0.94 for T1; r2=0.93 for T2), with a mean logarithmic difference of 0.040 for T1-weighted and -0.084 for T2-weighted images. The comparison of values extracted by semi-automatic and automatic segmentations were highly correlated. Conclusions Quantification of body composition values at MRI from T1-weighted and T2-weighted images was highly correlated to same values at CT, therefore quantitative values of body composition among patients who underwent either one of the examinations may be compared. CT body composition values extracted by semi-automatic and automatic segmentations showed high correlation

Cloud-Based Benchmarking of Medical Image Analysis

Medical imagin

Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images

Abstract Background The imaging assessment of paraspinal muscle morphology and fatty infiltration has gained considerable attention in the past decades, with reports suggesting an association between muscle degenerative changes and low back pain (LBP). To date, qualitative and quantitative approaches have been used to assess paraspinal muscle composition. Though highly reliable, manual thresholding techniques are time consuming and not always feasible in a clinical setting. The tedious and rater-dependent nature of such manual thresholding techniques provides the impetus for the development of automated or semi-automated segmentation methods. The purpose of the present study was to develop and evaluate an automated thresholding algorithm for the assessment of paraspinal muscle composition. The reliability and validity of the muscle measurements using the new automated thresholding algorithm were investigated through repeated measurements and comparison with measurements from an established, highly reliable manual thresholding technique. Methods Magnetic resonance images of 30 patients with LBP were randomly selected cohort of patients participating in a project on commonly diagnosed lumbar pathologies in patients attending spine surgeon clinics. A series of T2-weighted MR images were used to train the algorithm; preprocessing techniques including adaptive histogram equalization method image adjustment scheme were used to enhance the quality and contrast of the images. All muscle measurements were repeated twice using a manual thresholding technique and the novel automated thresholding algorithm, from axial T2-weigthed images, at least 5 days apart. The rater was blinded to all earlier measurements. Inter-method agreement and intra-rater reliability for each measurement method were assessed. The study did not received external funding and the authors have no disclosures. Results There was excellent agreement between the two methods with inter-method reliability coefficients (intraclass correlation coefficients) varying from 0.79 to 0.99. Bland and Altman plots further confirmed the agreement between the two methods. Intra-rater reliability and standard error of measurements were comparable between methods, with reliability coefficient varying between 0.95 and 0.99 for the manual thresholding and 0.97–0.99 for the automated algorithm. Conclusion The proposed automated thresholding algorithm to assess paraspinal muscle size and composition measurements was highly reliable, with excellent agreement with the reference manual thresholding method

Automatic Pancreas Segmentation and 3D Reconstruction for Morphological Feature Extraction in Medical Image Analysis

The development of highly accurate, quantitative automatic medical image segmentation techniques, in comparison to manual techniques, remains a constant challenge for medical image analysis. In particular, segmenting the pancreas from an abdominal scan presents additional difficulties: this particular organ has very high anatomical variability, and a full inspection is problematic due to the location of the pancreas behind the stomach. Therefore, accurate, automatic pancreas segmentation can consequently yield quantitative morphological measures such as volume and curvature, supporting biomedical research to establish the severity and progression of a condition, such as type 2 diabetes mellitus. Furthermore, it can also guide subject stratification after diagnosis or before clinical trials, and help shed additional light on detecting early signs of pancreatic cancer. This PhD thesis delivers a novel approach for automatic, accurate quantitative pancreas segmentation in mostly but not exclusively Magnetic Resonance Imaging (MRI), by harnessing the advantages of machine learning and classical image processing in computer vision. The proposed approach is evaluated on two MRI datasets containing 216 and 132 image volumes, achieving a mean Dice similarity coefficient (DSC) of 84:1 4:6% and 85:7 2:3% respectively. In order to demonstrate the universality of the approach, a dataset containing 82 Computer Tomography (CT) image volumes is also evaluated and achieves mean DSC of 83:1 5:3%. The proposed approach delivers a contribution to computer science (computer vision) in medical image analysis, reporting better quantitative pancreas segmentation results in comparison to other state-of-the-art techniques, and also captures detailed pancreas boundaries as verified by two independent experts in radiology and radiography. The contributions’ impact can support the usage of computational methods in biomedical research with a clinical translation; for example, the pancreas volume provides a prognostic biomarker about the severity of type 2 diabetes mellitus. Furthermore, a generalisation of the proposed segmentation approach successfully extends to other anatomical structures, including the kidneys, liver and iliopsoas muscles using different MRI sequences. Thus, the proposed approach can incorporate into the development of a computational tool to support radiological interpretations of MRI scans obtained using different sequences by providing a “second opinion”, help reduce possible misdiagnosis, and consequently, provide enhanced guidance towards targeted treatment planning