Automatic segmentation of the human thigh muscles in magnetic resonance imaging

Advances in magnetic resonance imaging (MRI) and analysis techniques have improved diagnosis and patient treatment pathways. Typically, image analysis requires substantial technical and medical expertise and MR images can su↵er from artefacts, echo and intensity inhomogeneity due to gradient pulse eddy currents and inherent e↵ects of pulse radiation on MRI radio frequency (RF) coils that complicates the analysis. Processing and analysing serial sections of MRI scans to measure tissue volume is an additional challenge as the shapes and the borders between neighbouring tissues change significantly by anatomical location. Medical imaging solutions are needed to avoid laborious manual segmentation of specified regions of interest (ROI) and operator errors. The work set out in this thesis has addressed this challenge with a specific focus on skeletal muscle segmentation of the thigh. The aim was to develop an MRI segmentation framework for the quadriceps muscles, femur and bone marrow. Four contributions of this research include: (1) the development of a semi-automatic segmentation framework for a single transverse-plane image; (2) automatic segmentation of a single transverseplane image; (3) the automatic segmentation of multiple contiguous transverse-plane images from a full MRI thigh scan; and (4) the use of deep learning for MRI thigh quadriceps segmentation. Novel image processing, statistical analysis and machine learning algorithms were developed for all solutions and they were compared against current gold-standard manual segmentation. Frameworks (1) and (3) require minimal input from the user to delineate the muscle border. Overall, the frameworks in (1), (2) and (3) o↵er very good output performance, with respective framework’s mean segmentation accuracy by JSI and processing time of: (1) 0.95 and 17 sec; (2) 0.85 and 22 sec; and (3) 0.93 and 3 sec. For the framework in (4), the ImageNet trained model was customized by replacing the fully-connected layers in its architecture to convolutional layers (hence the name of Fully Convolutional Network (FCN)) and the pre-trained model was transferred for the ROI segmentation task. With the implementation of post-processing for image filtering and morphology to the segmented ROI, we have successfully accomplished a new benchmark for thigh MRI analysis. The mean accuracy and processing time with this framework are 0.9502 (by JSI ) and 0.117 sec per image, respectively

Muscle volume quantification: guiding transformers with anatomical priors

Muscle volume is a useful quantitative biomarker in sports, but also for the follow-up of degenerative musculo-skelletal diseases. In addition to volume, other shape biomarkers can be extracted by segmenting the muscles of interest from medical images. Manual segmentation is still today the gold standard for such measurements despite being very time-consuming. We propose a method for automatic segmentation of 18 muscles of the lower limb on 3D Magnetic Resonance Images to assist such morphometric analysis. By their nature, the tissue of different muscles is undistinguishable when observed in MR Images. Thus, muscle segmentation algorithms cannot rely on appearance but only on contour cues. However, such contours are hard to detect and their thickness varies across subjects. To cope with the above challenges, we propose a segmentation approach based on a hybrid architecture, combining convolutional and visual transformer blocks. We investigate for the first time the behaviour of such hybrid architectures in the context of muscle segmentation for shape analysis. Considering the consistent anatomical muscle configuration, we rely on transformer blocks to capture the longrange relations between the muscles. To further exploit the anatomical priors, a second contribution of this work consists in adding a regularisation loss based on an adjacency matrix of plausible muscle neighbourhoods estimated from the training data. Our experimental results on a unique database of elite athletes show it is possible to train complex hybrid models from a relatively small database of large volumes, while the anatomical prior regularisation favours better predictions

The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs.

Automated semantic segmentation of multiple knee joint tissues is desirable to allow faster and more reliable analysis of large datasets and to enable further downstream processing e.g. automated diagnosis. In this work, we evaluate the use of conditional Generative Adversarial Networks (cGANs) as a robust and potentially improved method for semantic segmentation compared to other extensively used convolutional neural network, such as the U-Net. As cGANs have not yet been widely explored for semantic medical image segmentation, we analysed the effect of training with different objective functions and discriminator receptive field sizes on the segmentation performance of the cGAN. Additionally, we evaluated the possibility of using transfer learning to improve the segmentation accuracy. The networks were trained on i) the SKI10 dataset which comes from the MICCAI grand challenge "Segmentation of Knee Images 2010″, ii) the OAI ZIB dataset containing femoral and tibial bone and cartilage segmentations of the Osteoarthritis Initiative cohort and iii) a small locally acquired dataset (Advanced MRI of Osteoarthritis (AMROA) study) consisting of 3D fat-saturated spoiled gradient recalled-echo knee MRIs with manual segmentations of the femoral, tibial and patellar bone and cartilage, as well as the cruciate ligaments and selected peri-articular muscles. The Sørensen-Dice Similarity Coefficient (DSC), volumetric overlap error (VOE) and average surface distance (ASD) were calculated for segmentation performance evaluation. DSC ≥ 0.95 were achieved for all segmented bone structures, DSC ≥ 0.83 for cartilage and muscle tissues and DSC of ≈0.66 were achieved for cruciate ligament segmentations with both cGAN and U-Net on the in-house AMROA dataset. Reducing the receptive field size of the cGAN discriminator network improved the networks segmentation performance and resulted in segmentation accuracies equivalent to those of the U-Net. Pretraining not only increased segmentation accuracy of a few knee joint tissues of the fine-tuned dataset, but also increased the network's capacity to preserve segmentation capabilities for the pretrained dataset. cGAN machine learning can generate automated semantic maps of multiple tissues within the knee joint which could increase the accuracy and efficiency for evaluating joint health.European Union's Horizon 2020 Framework Programme [grant number 761214] Addenbrooke’s Charitable Trust (ACT) National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre University of Cambridge Cambridge University Hospitals NHS Foundation Trust GSK VARSITY: PHD STUDENTSHIP Funder reference: 300003198

Using MRI to elucidate the importance of physical activity to brain health and motor function in ageing

We live in an increasingly ageing population where people are living longer, but quality of life is not keeping up with increased lifespan. Concerns contributing to poor health in ageing include cognitive impairment and frailty driven by neurodegeneration and declining musculoskeletal health. Inactivity is also a significant driver of poor health in ageing and this may divulge important mechanisms of age-related decline. This thesis aims to differentiate whether activity in ageing impacts on physiological responses by studying trained and untrained individuals. Lifelong highly active male cyclists (Older Trained, OT), age-matched healthy older untrained (OU) males and young untrained (YU) males participants underwent cognitive and physiological tests, and MRI structural and functional scans of the muscle, heart and brain. Brain and heart functional measures were collected at rest, during supine exercise using a Ergospect Cardio-Step module, and during a recovery period post-exercise. A semi-automatic muscle segmentation method was developed to extract measures of leg muscle volume and fat fraction from wholebody mDIXON MRI scans. In general age was the primary driver of differences found rather than training. In the muscle, lower muscle strength was associated with older age. Additionally, the older untrained (OU) had greater fat percentages within their calf muscles than the young untrained (YU). The OT group were more similar to the YU group than the OU group in muscle quality during isokinetic contractions. For cardiac measures, peak heartrate during the VO2 test, aortic stroke volume and stroke distance were all lower in the OT and OU groups. Aortic stroke volume was higher in the older OT and OU groups than the YU group during supine exercise. Heartrate increased during exercise in all participants, but for the YU group this was the primary driver of their increase in cardiac output with no increase in stroke volume, whereas the older OT and OU groups compensated for lower increases in heartrate with concurrent increases in stroke volume. Aortic backflow was greater in older groups at rest, without increased aortic stiffening, suggesting greater peripheral vasculature resistance in the older groups. In the brain measures, gmCBF at baseline and cerebral vessel velocity at both baseline and during supine exercise were lower with age. OEF was higher in the OU group than YU group. As expected, white matter, cortical and subcortical grey matter volume, as well as cortical thickness were lower in the older groups than the young group. Modest correlations existed between cerebral velocity during supine exercise and cortical and sub-cortical grey matter, white matter and cortical thickness. Structural connectivity measured was greater in the young than older groups, along with the cognitive scores from the MOCA and Trail A tests. Some effects of a long term high physical activity were seen, where the OT group were more similar to the YU group than the OU group. This lifestyle effect was exhibited in maintained muscle quality during isokinetic contractions, higher VO2peaks, and greater velocity of cerebral blood flow at rest. All of the older volunteers in this thesis were non-frail, non-sedentary and healthy, and therefore differences of activity levels may not have been large enough to reveal significant impacts of a highly active lifestyle in a small sample size. Future work will increase the sample size, examine more differentiated groups or introduce an intervention and improve acquisition techniques for better data quality

Using MRI to elucidate the importance of physical activity to brain health and motor function in ageing

We live in an increasingly ageing population where people are living longer, but quality of life is not keeping up with increased lifespan. Concerns contributing to poor health in ageing include cognitive impairment and frailty driven by neurodegeneration and declining musculoskeletal health. Inactivity is also a significant driver of poor health in ageing and this may divulge important mechanisms of age-related decline. This thesis aims to differentiate whether activity in ageing impacts on physiological responses by studying trained and untrained individuals. Lifelong highly active male cyclists (Older Trained, OT), age-matched healthy older untrained (OU) males and young untrained (YU) males participants underwent cognitive and physiological tests, and MRI structural and functional scans of the muscle, heart and brain. Brain and heart functional measures were collected at rest, during supine exercise using a Ergospect Cardio-Step module, and during a recovery period post-exercise. A semi-automatic muscle segmentation method was developed to extract measures of leg muscle volume and fat fraction from wholebody mDIXON MRI scans. In general age was the primary driver of differences found rather than training. In the muscle, lower muscle strength was associated with older age. Additionally, the older untrained (OU) had greater fat percentages within their calf muscles than the young untrained (YU). The OT group were more similar to the YU group than the OU group in muscle quality during isokinetic contractions. For cardiac measures, peak heartrate during the VO2 test, aortic stroke volume and stroke distance were all lower in the OT and OU groups. Aortic stroke volume was higher in the older OT and OU groups than the YU group during supine exercise. Heartrate increased during exercise in all participants, but for the YU group this was the primary driver of their increase in cardiac output with no increase in stroke volume, whereas the older OT and OU groups compensated for lower increases in heartrate with concurrent increases in stroke volume. Aortic backflow was greater in older groups at rest, without increased aortic stiffening, suggesting greater peripheral vasculature resistance in the older groups. In the brain measures, gmCBF at baseline and cerebral vessel velocity at both baseline and during supine exercise were lower with age. OEF was higher in the OU group than YU group. As expected, white matter, cortical and subcortical grey matter volume, as well as cortical thickness were lower in the older groups than the young group. Modest correlations existed between cerebral velocity during supine exercise and cortical and sub-cortical grey matter, white matter and cortical thickness. Structural connectivity measured was greater in the young than older groups, along with the cognitive scores from the MOCA and Trail A tests. Some effects of a long term high physical activity were seen, where the OT group were more similar to the YU group than the OU group. This lifestyle effect was exhibited in maintained muscle quality during isokinetic contractions, higher VO2peaks, and greater velocity of cerebral blood flow at rest. All of the older volunteers in this thesis were non-frail, non-sedentary and healthy, and therefore differences of activity levels may not have been large enough to reveal significant impacts of a highly active lifestyle in a small sample size. Future work will increase the sample size, examine more differentiated groups or introduce an intervention and improve acquisition techniques for better data quality

Automated Strategies in Multimodal and Multidimensional Ultrasound Image-based Diagnosis

Medical ultrasonography is an effective technique in traditional anatomical and functional diagnosis. However, it requires the visual examination by experienced clinicians, which is a laborious, time consuming and highly subjective procedure. Computer-aided diagnosis (CADx) have been extensively used in clinical practice to support the interpretation of images; nevertheless, current ultrasound CADx still entails a substantial user-dependency and are unable to extract image data for prediction modelling. The aim of this thesis is to propose a set of fully automated strategies to overcome the limitations of ultrasound CADx. These strategies are addressed to multiple modalities (B-Mode, Contrast-Enhanced Ultrasound-CEUS, Power Doppler-PDUS and Acoustic Angiography-AA) and dimensions (2-D and 3-D imaging). The enabling techniques presented in this work are designed, developed and quantitively validated to efficiently improve the overall patients’ diagnosis. This work is subdivided in 2 macro-sections: in the first part, two fully automated algorithms for the reliable quantification of 2-D B-Mode ultrasound skeletal muscle architecture and morphology are proposed. In the second part, two fully automated algorithms for the objective assessment and characterization of tumors’ vasculature in 3-D CEUS and PDUS thyroid tumors and preclinical AA cancer growth are presented. In the first part, the MUSA (Muscle UltraSound Analysis) algorithm is designed to measure the muscle thickness, the fascicles length and the pennation angle; the TRAMA (TRAnsversal Muscle Analysis) algorithm is proposed to extract and analyze the Visible Cross-Sectional Area (VCSA). MUSA and TRAMA algorithms have been validated on two datasets of 200 images; automatic measurements have been compared with expert operators’ manual measurements. A preliminary statistical analysis was performed to prove the ability of texture analysis on automatic VCSA in the distinction between healthy and pathological muscles. In the second part, quantitative assessment on tumor vasculature is proposed in two automated algorithms for the objective characterization of 3-D CEUS/Power Doppler thyroid nodules and the evolution study of fibrosarcoma invasion in preclinical 3-D AA imaging. Vasculature analysis relies on the quantification of architecture and vessels tortuosity. Vascular features obtained from CEUS and PDUS images of 20 thyroid nodules (10 benign, 10 malignant) have been used in a multivariate statistical analysis supported by histopathological results. Vasculature parametric maps of implanted fibrosarcoma are extracted from 8 rats investigated with 3-D AA along four time points (TPs), in control and tumors areas; results have been compared with manual previous findings in a longitudinal tumor growth study. Performance of MUSA and TRAMA algorithms results in 100% segmentation success rate. Absolute difference between manual and automatic measurements is below 2% for the muscle thickness and 4% for the VCSA (values between 5-10% are acceptable in clinical practice), suggesting that automatic and manual measurements can be used interchangeably. The texture features extraction on the automatic VCSAs reveals that texture descriptors can distinguish healthy from pathological muscles with a 100% success rate for all the four muscles. Vascular features extracted of 20 thyroid nodules in 3-D CEUS and PDUS volumes can be used to distinguish benign from malignant tumors with 100% success rate for both ultrasound techniques. Malignant tumors present higher values of architecture and tortuosity descriptors; 3-D CEUS and PDUS imaging present the same accuracy in the differentiation between benign and malignant nodules. Vascular parametric maps extracted from the 8 rats along the 4 TPs in 3-D AA imaging show that parameters extracted from the control area are statistically different compared to the ones within the tumor volume. Tumor angiogenetic vessels present a smaller diameter and higher tortuosity. Tumor evolution is characterized by the significant vascular trees growth and a constant value of vessel diameter along the four TPs, confirming the previous findings. In conclusion, the proposed automated strategies are highly performant in segmentation, features extraction, muscle disease detection and tumor vascular characterization. These techniques can be extended in the investigation of other organs, diseases and embedded in ultrasound CADx, providing a user-independent reliable diagnosis