Plantar fascia segmentation and thickness estimation in ultrasound images

Ultrasound (US) imaging offers significant potential in diagnosis of plantar fascia (PF) injury and monitoring treatment. In particular US imaging has been shown to be reliable in foot and ankle assessment and offers a real-time effective imaging technique that is able to reliably confirm structural changes, such as thickening, and identify changes in the internal echo structure associated with diseased or damaged tissue. Despite the advantages of US imaging, images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. It is therefore a requirement to devise a system that allows better and easier interpretation of PF ultrasound images during diagnosis. This study proposes an automatic segmentation approach which for the first time extracts ultrasound data to estimate size across three sections of the PF (rearfoot, midfoot and forefoot). This segmentation method uses artificial neural network module (ANN) in order to classify small overlapping patches as belonging or not-belonging to the region of interest (ROI) of the PF tissue. Features ranking and selection techniques were performed as a post-processing step for features extraction to reduce the dimension and number of the extracted features. The trained ANN classifies the image overlapping patches into PF and non-PF tissue, and then it is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and area-length calculation algorithms. This new approach is capable of accurately segmenting the PF region, differentiating it from surrounding tissues and estimating its thickness

Finite element modelling of the foot for clinical application: A systematic review

Over the last two decades finite element modelling has been widely used to give new insight on foot and footwear biomechanics. However its actual contribution for the improvement of the therapeutic outcome of different pathological conditions of the foot, such as the diabetic foot, remains relatively limited. This is mainly because finite element modelling is only been used within the research domain. Clinically applicable finite element modelling can open the way for novel diagnostic techniques and novel methods for treatment planning/optimisation which would significantly enhance clinical practice. In this context this review aims to provide an overview of modelling techniques in the field of foot and footwear biomechanics and to investigate their applicability in a clinical setting. Even though no integrated modelling system exists that could be directly used in the clinic and considerable progress is still required, current literature includes a comprehensive toolbox for future work towards clinically applicable finite element modelling. The key challenges include collecting the information that is needed for geometry design, the assignment of material properties and loading on a patient-specific basis and in a cost-effective and non-invasive way. The ultimate challenge for the implementation of any computational system into clinical practice is to ensure that it can produce reliable results for any person that belongs in the population for which it was developed. Consequently this highlights the need for thorough and extensive validation of each individual step of the modelling process as well as for the overall validation of the final integrated system

Ultrasound Strain Elastography Reliability in the Assessment of the Plantar Fascia and Its Relationship with the Plantar Thickness in Healthy Adults: An Intra and Interobserver Reliability Study in Novice Evaluators

Partial funding for open access charge: Universidad de Málag

Estimating the material properties of heel pad sub-layers using inverse finite element analysis

Detailed information about the biomechanical behaviour of plantar heel pad tissue contributes to our understanding of load transfer when the foot impacts the ground. The objective of this work was to obtain the hyperelastic and viscoelastic material properties of heel pad sub-layers (skin, micro-chamber and macro-chamber layers) in-vivo. An anatomically detailed 3D Finite Element model of the human heel was used to derive the sub-layer material properties. A combined ultrasound imaging and motorised platform system was used to compress heel pad and to create input data for the Finite Element model. The force-strain responses of the heel pad and its sub-layers under slow compression (5mm/s) and rapid loading-hold-unloading cycles (225mm/s), were measured and hyperelastic and viscoelastic properties of the three heel pad sub-layers were estimated by the model. The loaded (under ~315N) thickness of the heel pad was measured from MR images and used for hyperelastic model validation. The capability of the model to predict peak plantar pressure was used for further validation. Experimental responses of the heel pad under different dynamic loading scenarios (loading-hold-unloading cycles at 141mm/s and sinusoidal loading with maximum velocity of 300mm/s) were used to validate the viscoelastic model. Good agreement was achieved between the predicted and experimental results for both hyperelastic (<6.4% unloaded thickness, 4.4% maximum peak plantar pressure) and viscoelastic (Root Mean Square errors for loading and unloading periods <14.7%, 5.8% maximum force) simulations. This paper provides the first definition of material properties for heel pad sub-layers by using in-vivo experimental force-strain data and an anatomically detailed 3D Finite Element model of the heel

Plantar fascia ultrasound images characterization and classification using support vector machine

The examination of plantar fascia (PF) ultrasound (US) images is subjective and based on the visual perceptions and manual biometric measurements carried out by medical experts. US images feature extraction, characterization and classification have been widely introduced for improving the accuracy of medical assessment, reducing its subjective nature and the time required by medical experts for PF pathology diagnosis. In this paper, we develop an automated supervised classification approach using the Support Vector Machine (Linear and Kernel) to distinguishes between symptomatic and asymptomatic PF cases. Such an approach will facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Six feature sets were extracted from the segmented PF region. Additionally, features normalization, features ranking and selection analysis using an unsupervised infinity selection method were introduced for the characterization and the classification of symptomatic and asymptomatic PF subjects. The performance of the classifiers was assessed using confusion matrix attributes and some derived performance measures including recall, specificity, balanced accuracy, precision, F-score and Matthew’s correlation coefficient. Using the best selected features sets, Linear SVM and Kernel SVM achieved an F-Score of 97.06 and 98.05 respectively

Thickness estimation, automated classification and novelty detection in ultrasound images of the plantar fascia tissues

The plantar fascia (PF) tissue plays an important role in the movement and the stability of the foot during walking and running. Thus it is possible for the overuse and the associated medical problems to cause injuries and some severe common diseases. Ultrasound (US) imaging offers significant potential in diagnosis of PF injuries and monitoring treatments. Despite the advantages of US, the generated PF images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. This limits the use of US in clinical practice and therefore impacts on patient services for what is a common problem and a major cause of foot pain and discomfort. It is therefore a requirement to devise an automated system that allows better and easier interpretation of PF US images during diagnosis. This study is concerned with developing a computer-based system using a combination of medical image processing techniques whereby different PF US images can be visually improved, segmented, analysed and classified as normal or abnormal, so as to provide more information to the doctors and the clinical treatment department for early diagnosis and the detection of the PF associated medical problems. More specifically, this study is required to investigate the possibility of a proposed model for localizing and estimating the PF thickness a cross three different sections (rearfoot, midfoot and forefoot) using a supervised ANN segmentation technique. The segmentation method uses RBF artificial neural network module in order to classify small overlapping patches into PF and non-PF tissue. Feature selection technique was performed as a post-processing step for feature extraction to reduce the number of the extracted features. Then the trained RBF-ANN is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and a proposed area-length calculation algorithm. Additionally, different machine learning approaches were investigated and applied to the segmented PF region in order to distinguish between symptomatic and asymptomatic PF subjects using the best normalized and selected feature set. This aims to facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Finally, a novelty detection framework for detecting the symptomatic PF samples (with plantar fasciitis disorder) using only asymptomatic samples is proposed. This model implies the following: feature analysis, building a normality model by training the one-class SVDD classifier using only asymptomatic PF training datasets, and computing novelty scores using the trained SVDD classifier, training and testing asymptomatic datasets, and testing symptomatic datasets of the PF dataset. The performance evaluation results showed that the proposed approaches used in this study obtained favourable results compared to other methods reported in the literature

Process of estimating the material properties of human heel pad sub-layers using inverse finite element analysis and some model applications

The human heel pad is subject to repetitive loading and plays an important role in absorbing shocks which may cause injuries. The heel pad has a composite biological structure consistingof the fat pad and the skin. The fat pad tissue is organised into a superficial micro-chamber layer and a deep macro-chamber layer.The heel pad sub-layers have different structures and properties. Hence, to understand the contribution of each layer to the heel problem, it is essential to develop a model with discrete structure. Currently, only plantar pressure measurements are used for diagnosis and treatment of the heel problems, whereas it has been shown that high internal tissue stress is an important factor. Because of complex geometry, discrete structure and nonlinear material behaviour of the heel pad, the external force applied to the heel may result in inhomogeneous internal stress condition. Therefore, the relationship between the plantar pressure and internal stress does not seem to be simple. Since there is no equipment to allow measurement of internal stress, a detailed multi-layered FE model of the heel pad can be used as a solution to predict the internal stress.The main objective of this work was to obtain the hyperelastic and viscoelastic material properties of the subject-specific heel pad sub-layers in-vivo. For this purpose, a combined methodology of finite element modeling and experimentation was developed. An anatomically detailed 3D FE model of the human heel area was developed using MR images of the right foot of a female subject. A combined ultrasound and indentation systemwas used to apply series of slow and rapid compression tests on the same foot. The forcestrain responses of the heel pad and its sub-layers were used as input to the FE model to estimate properties of the heel pad sub-layers using inverse FEA. The hyperelastic and viscoelastic FE models were then implemented to investigate the effectsof experimental and geometrical factors on the heel pad responses. The model was also used to assess the robustness of the hyperelastic FE model when predicting the behaviour of other heels with different geometries. Finally, this model was used with Taguchi method to evaluatethe effect of footwear design factors on the compressive stress in the heel pad tissue. There were some key limitations in this study. For example, the properties of the heel pad sub-layers were estimated only for a specific heel pad. Also, whilst it is preferred to use xviiiautomatic segmentation and solid modeling to improve repeatability of some FE processes, some parts of the modeling process were performed manually