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Ma, Jingting; Wang, Anqi; Lin, Feng; Wesarg, Stefan; Erdt, Marius

A novel robust kernel principal component analysis for nonlinear statistical shape modeling from erroneous data

2019

Computerized Medical Imaging and Graphics

Statistical Shape Models (SSMs) have achieved considerable success in medical image segmentation. A high quality SSM is able to approximate the main plausible variances of a given anatomical structure to guide segmentation. However, it is technically challenging to derive such a quality model because: (1) the distribution of shape variance is often nonlinear or multi-modal which cannot be modeled by standard approaches assuming Gaussian distribution; (2) as the quality of annotations in training data usually varies, heavy corruption will degrade the quality of the model as a whole. In this work, these challenges are addressed by introducing a generic SSM that is able to model nonlinear distribution and is robust to outliers in training data. Without losing generality and assuming a sparsity in nonlinear distribution, a novel Robust Kernel Principal Component Analysis (RKPCA) for statistical shape modeling is proposed with the aim of constructing a low-rank nonlinear subspace where outliers are discarded. The proposed approach is validated on two different datasets: a set of 30 public CT kidney pairs and a set of 49 MRI ankle bones volumes. Experimental results demonstrate a significantly better performance on outlier recovery and a higher quality of the proposed model as well as lower segmentation errors compared to the state-of-the-art techniques.

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Montefiori, Erica; Modenese, Luca; Di Marco, Roberto; Magni-Manzoni, Silvia; Malattia, Clara; Petrarca, Maurizio; Ronchetti, Anna; Tanturri De Horatio, Laura; Dijkhuizen, Pieter van; Wang, Anqi; Wesarg, Stefan; Viceconti, Marco; Mazzà, Claudia

An Image-based Kinematic Model of the Tibiotalar and Subtalar Joints and its Application to Gait Analysis in Children with Juvenile Idiopathic Arthritis

2019

Journal of Biomechanics

In vivo estimates of tibiotalar and the subtalar joint kinematics can unveil unique information about gait biomechanics, especially in the presence of musculoskeletal disorders affecting the foot and ankle complex. Previous literature investigated the ankle kinematics on ex vivo data sets, but little has been reported for natural walking, and even less for pathological and juvenile populations. This paper proposes an MRI-based morphological fitting methodology for the personalised definition of the tibiotalar and the subtalar joint axes during gait, and investigated its application to characterise the ankle kinematics in twenty patients affected by Juvenile Idiopathic Arthritis (JIA). The estimated joint axes were in line with in vivo and ex vivo literature data and joint kinematics variation subsequent to inter-operator variability was in the order of 1°. The model allowed to investigate, for the first time in patients with JIA, the functional response to joint impairment. The joint kinematics highlighted changes over time that were consistent with changes in the patient’s clinical pattern and notably varied from patient to patient. The heterogeneous and patient-specific nature of the effects of JIA was confirmed by the absence of a correlation between a semi-quantitative MRI-based impairment score and a variety of investigated joint kinematics indexes. In conclusion, this study showed the feasibility of using MRI and morphological fitting to identify the tibiotalar and subtalar joint axes in a non-invasive patient-specific manner. The proposed methodology represents an innovative and reliable approach to the analysis of the ankle joint kinematics in pathological juvenile populations.

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Montefiori, Erica; Modenese, Luca; Di Marco, Roberto; Magni-Manzoni, Silvia; Malattia, Clara; Petrarca, Maurizio; Ronchetti, Anna; Tanturri De Horatio, Laura; Dijkhuizen, Pieter van; Wang, Anqi; Wesarg, Stefan; Viceconti, Marco; Mazzà, Claudia

Linking Joint Impairment and Gait Biomechanics in Patients with Juvenile Idiopathic Arthritis

2019

Annals of Biomedical Engineering

Juvenile Idiopathic Arthritis (JIA) is a paediatric musculoskeletal disease of unknown aetiology, leading to walking alterations when the lower-limb joints are involved. Diagnosis of JIA is mostly clinical. Imaging can quantify impairments associated to inflammation and joint damage. However, treatment planning could be better supported using dynamic information, such as joint contact forces (JCFs). To this purpose, we used a musculoskeletal model to predict JCFs and investigate how JCFs varied as a result of joint impairment in eighteen children with JIA. Gait analysis data and magnetic resonance images (MRI) were used to develop patient-specific lower-limb musculoskeletal models, which were evaluated for operator-dependent variability (< 3.6°, 0.05 N kg21 and 0.5 BW for joint angles, moments, and JCFs, respectively). Gait alterations and JCF patterns showed high between-subjects variability reflecting the pathology heterogeneity in the cohort. Higher joint impairment, assessed with MRI-based evaluation, was weakly associated to overall joint overloading. A stronger correlation was observed between impairment of one limb and overload of the contralateral limb, suggesting risky compensatory strategies being adopted, especially at the knee level. This suggests that knee overloading during gait might be a good predictor of disease progression and gait biomechanics should be used to inform treatment planning.

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Wang, Anqi; Franke, Andreas; Wesarg, Stefan

Semi-automatic Segmentation of JIA-induced Inflammation in MRI Images of Ankle Joints

2019

Medical Imaging 2019: Image Processing

SPIE Medical Imaging Symposium <2019, San Diego, CA, USA>

Proceedings of SPIE, 10949

The autoimmune disease Juvenile Idiopathic Arthritis (JIA) affects children of under 16 years and leads to the symptom of inflamed synovial membranes in affected joints. In clinical practice, characteristics of these inflamed membranes are used to stage the disease progression and to predict erosive bone damage. Manual outlining of inflammatory regions in each slide of a MRI dataset is still the gold standard for detection and quantification, however, this process is very tiresome and time-consuming. In addition, the inter- and intra-observer variability is a known problem of human annotators. We have developed the first method to detect inflamed regions in and around major joints in the human ankle. First, we use an adapted coupled shape model framework to segment the ankle bones in a MRI dataset. Based on these segmentations, joints are defined as locations where two bones are particularly close to each other. A number of potential inflammation candidates are generated using multi-level thresholding. Since it is known that inflamed synovial membranes occur in the proximity of joints, we filter out structures with similar intensities such as vessels and tendons sheaths using not only a vesselness filter, but also their distance to the joints and their size. The method has been evaluated on a set of 10 manually annotated clinical MRI datasets and achieved the following results: Precision 0.6785 ± 0.1584, Recall 0.5388 ± 0.1213, DICE 0.5696 ± 0.0976.

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Modenese, Luca; Montefiori, Erica; Wang, Anqi; Wesarg, Stefan; Viceconti, Marco; Mazzà, Claudia

Investigation of the Dependence of Joint Contact Forces on Musculotendon Parameters Using a Codified Workflow for Image-based Modelling

2018

Journal of Biomechanics

The generation of subject-specific musculoskeletal models of the lower limb has become a feasible task thanks to improvements in medical imaging technology and musculoskeletal modelling software. Nevertheless, clinical use of these models in paediatric applications is still limited for what concerns the estimation of muscle and joint contact forces. Aiming to improve the current state of the art, a methodology to generate highly personalized subject-specific musculoskeletal models of the lower limb based on magnetic resonance imaging (MRI) scans was codified as a step-by-step procedure and applied to data from eight juvenile individuals. The generated musculoskeletal models were used to simulate 107 gait trials using stereophotogrammetric and force platform data as input. To ensure completeness of the modelling procedure, muscles’ architecture needs to be estimated. Four methods to estimate muscles’ maximum isometric force and two methods to estimate musculotendon parameters (optimal fiber length and tendon slack length) were assessed and compared, in order to quantify their influence on the models’ output. Reported results represent the first comprehensive subject-specific model-based characterization of juvenile gait biomechanics, including profiles of joint kinematics and kinetics, muscle forces and joint contact forces. Our findings suggest that, when musculotendon parameters were linearly scaled from a reference model and the muscle force-length-velocity relationship was accounted for in the simulations, realistic knee contact forces could be estimated and these forces were not sensitive the method used to compute muscle maximum isometric force.

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Ma, Jingting; Wang, Anqi; Lin, Feng; Wesarg, Stefan; Erdt, Marius

Nonlinear Statistical Shape Modeling for Ankle Bone Segmentation Using a Novel Kernelized Robust PCA

2017

Medical Image Computing and Computer Assisted Intervention - MICCAI 2017: Part I

International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) <20, 2017, Québec City, QC, Canada>

Statistical shape models (SSMs) are widely employed in medical image segmentation. However, an inferior SSM will degenerate the quality of segmentations. It is challenging to derive an efficient model because: (1) often the training datasets are corrupted by noise and/or artifacts; (2) conventional SSM is not capable to capture nonlinear variabilities of a population of shape. Addressing these challenges, this work aims to create SSMs that are not only robust to abnormal training data but also satisfied with nonlinear distribution. As Robust PCA is an efficient tool to seek a clean low-rank linear subspace, a novel kernelized Robust PCA (KRPCA) is proposed to cope with nonlinear distribution for statistical shape modeling. In evaluation, the built nonlinear model is used in ankle bone segmentation where 9 bones are separately distributed. Evaluation results show that the model built with KRPCA has a significantly higher quality than other state-of-the-art methods.

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Wang, Anqi; Noll, Matthias; Wesarg, Stefan

Tumorsegmentierung in CD3/CD8-gefärbten Histopathologien

2015

Bildverarbeitung für die Medizin 2015

Workshop Bildverarbeitung für die Medizin (BVM) <18, 2015, Lübeck, Germany>

Segmentierung von bestimmten Gewebetypen in Histopathologien ist eine oft untersuchte Fragestellung. Üblicherweise werden dafür Gewebeproben mit Hämatoxylin-Eosin(HE)-Färbung verwendet. CD3/CD8-F¨arbungen hingegen sind nötig zur Sichtbarmachung von Immunzellen, differenzieren aber nur wenig zwischen unterschiedlichen Gewebearten. Vorteilhaft wäre es, wenn aus nur einem Gewebeschnitt mit einer bestimmten Färbung beide Informationen extrahiert werden könnten. In dieser Arbeit stellen wir ein Segmentierungsverfahren auf CD3/CD8-gef¨arbten Gewebeproben vor, das effizient zu berechnende und gleichzeitig aussagekräftige Features als Eingabe für einen Clustering- Algorithmus verwendet. In der Evaluation wird ein durchschnittlicher Accuracy-Wert von 94,44% erzielt. Dieser Wert ist vergleichbar mit den Ergebnissen verwandter State of the Art Methoden, die HE-gefärbte Proben einsetzen.

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Wang, Anqi; Sakas, Georgios [1. Gutachter]; Noll, Matthias [2. Gutachter]

Detektion von Tumorgewebe und invasiver Tumorgrenze in CD3/CD8 gefärbten Gewebeschnitten (Histopathologien)

2014

Darmstadt, TU, Master Thesis, 2014

Konventionelle Krankheitsprognose bei Krebserkrankungen basiert auf Größe des Tumors, Auftrittsort von Krebszellen und Vorliegen von Metastasen. Diese Anzeichen lassen jedoch keine Aussage über den postoperativen Krankheitsverlauf zu. Erste Studien haben ergeben, dass in solchen Fällen die Betrachtung der Immunantwort des Körpers eine zuverlässigere Vorhersage treffen kann. Die Immunantwort drückt sich in Art und Auftrittshäufigkeit von Immunzellen (sogenannte T-Zellen) in und um den Tumor aus. Zur Validierung dieser These wird in der vorliegenden Arbeit ein Verfahren entwickelt, das automatisch auf CD3/CD8-gefärbten histopathologischen Aufnahmen den Tumor und die invasive Tumorgrenze segmentiert. In Kombination mit einer anderen Arbeit, die ein Verfahren zur Zellenzählung implementiert, sollen große Datenmengen von Patienten evaluiert werden, deren Krankheitsverlauf bekannt ist. Die größte Herausforderung dieser Arbeit besteht im verwendeten Material. Üblicherweise wird Gewebe auf HE-gefärbten Aufnahmen segmentiert. CD3/CD8 sind hingegen Färbungen, welche T-Zellen klar erkennbar darstellen, aber unterschiedliche Gewebearten nur wenig differenzieren. Eine zusätzliche Schwierigkeit ist die Entwicklung des Verfahrens bei einer kleinen Menge an verfügbaren Trainings- und Testdaten. Aus der Aufgabenstellung ergeben sich für das Verfahren die Anforderungen Geschwindigkeit (Evaluation großer Datenmengen) und Genauigkeit der Segmentierung. Die Geschwindigkeitsanforderung wird erfüllt, indem effizient zu berechnende und gleichzeitig aussagekräftige Features als Eingabe für ein Clustering-Algorithmus verwendet werden. Die Verarbeitung einer Aufnahme dauert durchschnittlich 5 Minuten. Gemessen an dem Zeitaufwand eines Pathologen für die gleiche Aufgabe ist dies eine große Zeitersparnis. Die Evaluation ergab einen durchschnittlichen Accuracy-Wert von 0,94. Dieser Wert ist vergleichbar mit den Ergebnissen verwandter State of the Art Methoden, welche auf HE-gefärbten Aufnahmen arbeiten.