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

The application of b-mode ultrasonography for analysis of human skeletal muscle

Skeletal muscles control the joints of the skeletal system and they allow human movement and interaction with the environment. They are vital for stability in balance, walking and running, and many other skilled motor tasks. To understand how muscles operate in general and specific situations there are a variety of tools at the disposal of research scientists and clinicians for analysing muscle function. Strain gauges for example allow the quantification of forces exerted during joint rotation. However, skeletal muscles are multilayer systems and often different muscles are responsible for the overall force generated during joint rotation. Therefore, strain gauges do not reveal the extent of the contribution of individual muscles during muscle function. The most widely-used and accepted muscle analysis tool is electromyography (EMG), which can measure the activation level of individual muscles by measuring the electrical potential propagating through muscle resulting from local activations of motor units. However, EMG does not linearly relate to any real physical forces, meaning that without prior knowledge of the force exertion on the level of the muscle, force cannot be estimated. EMG can measure superficial layers of muscle non-invasively by attaching surface electrodes (surface EMG) to the skin over the belly of the muscle. To measure the activity of individual muscle beneath the superficial muscle, a needle or thin-wire electrode must be inserted through the skin and into the muscle volume (intramuscular EMG), which is invasive and not practical in many situations. Furthermore, intramuscular EMG can only provide measurement of a very small volume (<1mm3) which can have varying amounts of active motor units. Ultrasonography is a powerful cost-effective non-invasive imaging technology which allows real-time observation of cross-sections of multiple layers of dynamic skeletal muscle. Recent advances in automated skeletal muscle ultrasound analysis techniques, and advances in image processing techniques make ultrasound a valuable line of investigation for analysis of dynamic skeletal muscle. This aim of this thesis is to study and develop advanced image analysis techniques applicable to the analysis of dynamic skeletal muscle. The broader aim is to understand the capacity/limits of ultrasound as a skeletal muscle analysis tool. The ideas presented within offer new approaches to modelling complex muscle architecture and function via ultrasound. Tools have also been developed here that will contribute to, and promote ultrasound skeletal muscle analysis as a new and emerging technology which may be used by clinicians and research scientists to develop our understanding of skeletal muscle function. The main findings of this thesis are that automated segmentation of architecturally simple and complex skeletal muscle groups is possible and accurate, and that information about joint angles and muscle activity/force can be automatically extracted directly from ultrasound images without the explicit knowledge of how to extract it. The techniques used offer new possibilities for non-invasive information extraction from complex muscle groups such as the muscles in the human posterior neck

Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks

This paper presents an investigation into the feasibility of using deep learning methods for developing arbitrary full spatial resolution regression analysis of B-mode ultrasound images of human skeletal muscle. In this study, we focus on full spatial analysis of muscle fibre orientation, since there is an existing body of work with which to compare results. Previous attempts to automatically estimate fibre orientation from ultrasound are not adequate, often requiring manual region selection, feature engineering, providing low-resolution estimations (one angle per muscle) and deep muscles are often not attempted. We build upon our previous work in which automatic segmentation was used with plain convolutional neural network (CNN) and deep residual convolutional network (ResNet) architectures, to predict a low-resolution map of fibre orientation in extracted muscle regions. Here, we use deconvolutions and max-unpooling (DCNN) to regularise and improve predicted fibre orientation maps for the entire image, including deep muscles, removing the need for automatic segmentation and we compare our results with the CNN and ResNet, as well as a previously established feature engineering method, on the same task. Dynamic ultrasound images sequences of the calf muscles were acquired (25 Hz) from 8 healthy volunteers (4 male, ages: 25–36, median 30). A combination of expert annotation and interpolation/extrapolation provided labels of regional fibre orientation for each image. Neural networks (CNN, ResNet, DCNN) were then trained both with and without dropout using leave one out cross-validation. Our results demonstrated robust estimation of full spatial fibre orientation within approximately 6◦ error, which was an improvement on previous methods

Ultrasonography for the prediction of musculoskeletal function

Ultrasound (US) imaging is a well-recognised technique for studying in vivo characteristics of a range of biological tissues due to its portability, low cost and ease of use; with recent technological advances that increased the range of choices regarding acquisition and analysis of ultrasound data available for studying dynamic behaviour of different tissues. This thesis focuses on the development and validation of methods to exploit the capabilities of ultrasound technology to investigate dynamic properties of skeletal muscles in vivo exclusively using ultrasound data. The overarching aim was to evaluate the influence of US data properties and the potential of inference algorithms for prediction of net ankle joint torques. A fully synchronised experimental setup was designed and implemented enabling collection of US, Electromyography (EMG) and dynamometer data from the Gastrocnemius medialis muscle and ankle joint of healthy adult volunteers. Participants performed three increasing complexity muscle movement tasks: passive joint rotations, isometric and isotonic contractions. Two frame rates (32 and 1000 fps) and two data precisions (8 and 16-bits) were obtained enabling analysis of the impact of US data temporal resolution and precision on joint torque predictions. Predictions of net joint torque were calculated using five data inference algorithms ranging from simple regression through to Artificial Neural Networks. Results indicated that accurate predictions of net joint torque can be obtained from the analysis of ultrasound data of one muscle. Significantly improved predictions were observed using the faster frame rate during active tasks, with 16-bit data precision and ANN further improving results in isotonic movements. The speed of muscle activation and complexity of fluctuations of the resulting net joint torques were key factors underpinning the prediction errors recorded. The properties of collected US data in combination with the movement tasks to be assessed should therefore be a key consideration in the development of experimental protocols for in vivo assessment of skeletal muscles

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 267, January 1985

This publication is a cumulative index to the abstracts contained in the Supplements 255 through 266 of Aerospace Medicine and Biology: A Continuing Bibliography. It includes seven indexes--subject, personal author, corporate source, foreign technology, contract number, report number, and accession number