107 research outputs found

    Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images

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    In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes multiple landmarks through the analysis of image patches, performing regression and classification simultaneously. In regression, displacement vectors pointing from the center of image patches towards landmark locations are determined. In classification, presence of landmarks of interest in the patch is established. Global landmark locations are obtained by averaging the predicted displacement vectors, where the contribution of each displacement vector is weighted by the posterior classification probability of the patch that it is pointing from. Subsequently, for each landmark localized with global localization, local analysis is performed. Specialized FCNNs refine the global landmark locations by analyzing local sub-images in a similar manner, i.e. by performing regression and classification simultaneously and combining the results. Evaluation was performed through localization of 8 anatomical landmarks in CCTA scans, 2 landmarks in olfactory MR scans, and 19 landmarks in cephalometric X-rays. We demonstrate that the method performs similarly to a second observer and is able to localize landmarks in a diverse set of medical images, differing in image modality, image dimensionality, and anatomical coverage.Comment: 12 pages, accepted at IEEE transactions in Medical Imagin

    CNN-based Landmark Detection in Cardiac CTA Scans

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    Fast and accurate anatomical landmark detection can benefit many medical image analysis methods. Here, we propose a method to automatically detect anatomical landmarks in medical images. Automatic landmark detection is performed with a patch-based fully convolutional neural network (FCNN) that combines regression and classification. For any given image patch, regression is used to predict the 3D displacement vector from the image patch to the landmark. Simultaneously, classification is used to identify patches that contain the landmark. Under the assumption that patches close to a landmark can determine the landmark location more precisely than patches farther from it, only those patches that contain the landmark according to classification are used to determine the landmark location. The landmark location is obtained by calculating the average landmark location using the computed 3D displacement vectors. The method is evaluated using detection of six clinically relevant landmarks in coronary CT angiography (CCTA) scans: the right and left ostium, the bifurcation of the left main coronary artery (LM) into the left anterior descending and the left circumflex artery, and the origin of the right, non-coronary, and left aortic valve commissure. The proposed method achieved an average Euclidean distance error of 2.19 mm and 2.88 mm for the right and left ostium respectively, 3.78 mm for the bifurcation of the LM, and 1.82 mm, 2.10 mm and 1.89 mm for the origin of the right, non-coronary, and left aortic valve commissure respectively, demonstrating accurate performance. The proposed combination of regression and classification can be used to accurately detect landmarks in CCTA scans.Comment: This work was submitted to MIDL 2018 Conferenc

    Data-efficient knee anatomical landmark localization using deep learning

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    Abstract. Knee osteoarthritis (OA) is the most common musculoskeletal degenerative disease affecting the joints. OA is examined at a doctor’s visit and an X-ray image is often used to confirm the diagnosis. There is no treatment available for OA, therefore it is important to diagnose knee osteoarthritis at the earliest possible stage to preventpossible complications. Traditional methods used by a practitioners do not detect osteoarthritis as early as possible, therefore other methods are needed for early diagnosis. One possibility is to use novel quantitative imaging biomarkers, computation of which often requires precise understanding of the knee anatomy by a computer. More specifically, it is important to locate different areas of the knee according to anatomical atlases and place relevant regions of interest to compute the imaging biomarkers. A state-of-the-art approach for this problem is based on anatomical landmark localization. In this work, the localization of anatomical landmarks from knee X-rays using deep learning is investigated. To date, statistical methods have been used to localize landmarks, but this work focuses on identification based on deep learning and investigates how the amount of available training data affects performance. The method investigated in the present thesis is based on the KNEEL method developed earlier at the University of Oulu. The aim of this work was to improve this method by adjusting the training parameters and leveraging equivalent regularization for semi-supervised learning. Images from the Osteoarthritis Initiative database were used as material for training and validation. During the work, it was found that by adjusting the parameters used for training, anatomical landmarks can be localized more accurately than in the original KNEEL method. By adding the equivalent regularization, the accuracy of the localization was increased substantially, and a further enhancement in performance can be observed by utilizing unlabeled data in a semi-supervised learning manner. The results, developed in this thesis can layer be leveraged in OA research or even clinical practice, where the computation of quantitative imaging biomarkers is important. To our knowledge, this is the first work in OA where SSL and equivariant regularization were used.Datatehokas polven anatomisten maamerkkien paikantaminen käyttäen syväoppimista. Tiivistelmä. Polven nivelrikko on yleisin niveliin vaikuttava tuki- ja liikuntaelimistöä rappeuttava sairaus. Nivelrikko tutkitaan lääkärikäynnin yhteydessä ja diagnoosi vahvistetaan usein röntgenkuvantamisen avulla. Nivelrikkoon ei ole saatavilla hoitoa, joten on tärkeää diagnosoida polven nivelrikko mahdollisimman varhaisessa vaiheessa mahdollisten komplikaatioiden välttämiseksi. Perinteiset lääkäreiden käyttämät menetelmät eivät tunnista nivelrikkoa riittävän aikaisin, siksi tarvitaan muita menetelmiä varhaisempaan diagnostiikkaan. Yksi mahdollisuus on käyttää kvantitatiivisia kuvantamisbiomarkkereita, mutta näiden laskemiseksi tietokoneen täytyy ymmärtää anatomisia rakenteita tarkasti. Tarkemmin sanottuna on tärkeää paikantaa polven eri rakenteet ihmisen anatomiasta ja merkitä kiinnostavat rakenteet, jotta kuvantamisbiomarkkerit voidaan laskea. Nykyisin tätä ongelmaa lähestytään anatomisten maamerkkien paikantamisen avulla. Tässä työssä tutkittiin anatomisten maamerkkien paikantamista polven röntgenkuvista syväoppimisen avulla. Perinteisesti tähän on käytetty staattisia menetelmiä, mutta tässä työssä keskityttiin paikantamiseen käyttäen syväoppimista ja tutkittiin kuinka käytettävissä oleva opetusdatan määrä vaikuttaa suorituskykyyn. Työssä käytetty metodi perustuu aikaisemmin Oulun yliopistossa kehitettyyn KNEEL metodiin. Tämän työn tarkoituksena oli parantaa tätä metodia säätämällä opetusparametreja sekä hyödyntää ekvivalenttia regularisaatiota syväoppimisen yhteydessa. Kuvia The Osteoarthritis Initiative -tietokannasta käytettiin opetukseen ja validointiin. Työn aikana havaittiin, että säätämällä opetukseen käytettäviä parametrejä, voidaan anatomiset maamerkit paikantaa tarkemmin kuin alkuperäisellä KNEEL metodilla. Ekvivalentin regularisaation lisäämisellä paikantamisen tarkkuus lisääntyi huomattavasti. Suorituskyky parani entisestään käyttämällä annotoimatonta dataa puoli-ohjatun oppimisen yhteydessä. Tämän opinnäytetyön yhteydessä kehitettyä metodia voidaan käyttää nivelrikon tutkimuksen yhteydessä tai kliinisessä käytössä, missä kvantitatiivisten kuvantamisbiomarkkereiden käyttö on tärkeää. Tietojemme mukaan tämä työ on ensimmäinen, jossa käytetään puoliohjattua oppimista sekä ekvivalenttia regularisaatiota nivelrikon yhteydessä

    Fully automatic cervical vertebrae segmentation framework for X-ray images

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    This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The cervical spine is a highly flexible anatomy and therefore vulnerable to injuries. Unfortunately, a large number of injuries in lateral cervical X-ray images remain undiagnosed due to human errors. Computer-aided injury detection has the potential to reduce the risk of misdiagnosis. Towards building an automatic injury detection system, in this paper, we propose a deep learning-based fully automatic framework for segmentation of cervical vertebrae in X-ray images. The framework first localizes the spinal region in the image using a deep fully convolutional neural network. Then vertebra centers are localized using a novel deep probabilistic spatial regression network. Finally, a novel shape-aware deep segmentation network is used to segment the vertebrae in the image. The framework can take an X-ray image and produce a vertebrae segmentation result without any manual intervention. Each block of the fully automatic framework has been trained on a set of 124 X-ray images and tested on another 172 images, all collected from real-life hospital emergency rooms. A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved

    Radiographic Assessment of Hip Disease in Children with Cerebral Palsy: Development of a Core Measurement Set and Analysis of an Artificial Intelligence System

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    Cerebral palsy is the most common physical disability during childhood. Cerebral palsy related hip disease is caused by an imbalance of muscle forces, resulting in progressive migration of the hip to complete dislocation. This can decrease function and quality of life. The prevention of hip dislocation is possible if detected early. Therefore, surveillance programmes have been set up to monitor children with cerebral palsy enabling clinicians to intervene early and improve outcomes. Currently, hip disease is assessed by analysing pelvic radiographs with various geometric measurements. This time-consuming task is undertaken frequently when monitoring a child with cerebral palsy. This thesis aimed to identify the key radiographic parameters used by clinicians (the core measurement set), and then build an artificial intelligence system to automate the calculation of this core measurement set. A systematic review was conducted identifying a comprehensive list of previously reported measurements from studies measuring radiographic outcomes in cerebral palsy children with hip pathologies. Fifteen measurements were identified from the systematic review, of which Reimers’ migration percentage was the most commonly reported. These measurements were used to perform a two-round Delphi study among orthopaedic surgeons and physiotherapists. Participants rated the importance of each measurement using a nine-point Likert scale (‘not important’ to critically important’). After the two rounds of the Delphi process, Reimers’ migration percentage was included in the core measurement set. Following the final consensus meeting, the femoral head-shaft angle was also included. The anteroposterior pelvic radiographs of 1650 children were then used to build an artificial intelligence system integrating the core measurement set, in collaboration with engineers from the University of Manchester. The newly developed artificial intelligence system was assessed by comparing its ability to calculate measurements and outline the pelvis and femur on a radiograph. The reliability of the dataset used to train the model was also analysed. The proposed artificial intelligence model achieved a ‘good to excellent’ inter-observer reliability across 450 radiographs when comparing its ability to calculate Reimers’ migration percentage to five clinicians. Its ability to outline the pelvis and proximal femur was ‘adequate’ with the better performance observed in the pelvis than the femur. The reliability of the training dataset used to teach the artificial intelligence model was ‘good’ to ‘very good’. Artificial intelligence systems are feasible solutions to optimise the efficiency of hip radiograph analysis in cerebral palsy. Studies are warranted to include the core measurement set as a minimum when reporting on hip disease in cerebral palsy. Future research should investigate the feasibility of implementing a risk score to predict the likelihood of hip displacement

    ADVANCED MOTION MODELS FOR RIGID AND DEFORMABLE REGISTRATION IN IMAGE-GUIDED INTERVENTIONS

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    Image-guided surgery (IGS) has been a major area of interest in recent decades that continues to transform surgical interventions and enable safer, less invasive procedures. In the preoperative contexts, diagnostic imaging, including computed tomography (CT) and magnetic resonance (MR) imaging, offers a basis for surgical planning (e.g., definition of target, adjacent anatomy, and the surgical path or trajectory to the target). At the intraoperative stage, such preoperative images and the associated planning information are registered to intraoperative coordinates via a navigation system to enable visualization of (tracked) instrumentation relative to preoperative images. A major limitation to such an approach is that motions during surgery, either rigid motions of bones manipulated during orthopaedic surgery or brain soft-tissue deformation in neurosurgery, are not captured, diminishing the accuracy of navigation systems. This dissertation seeks to use intraoperative images (e.g., x-ray fluoroscopy and cone-beam CT) to provide more up-to-date anatomical context that properly reflects the state of the patient during interventions to improve the performance of IGS. Advanced motion models for inter-modality image registration are developed to improve the accuracy of both preoperative planning and intraoperative guidance for applications in orthopaedic pelvic trauma surgery and minimally invasive intracranial neurosurgery. Image registration algorithms are developed with increasing complexity of motion that can be accommodated (single-body rigid, multi-body rigid, and deformable) and increasing complexity of registration models (statistical models, physics-based models, and deep learning-based models). For orthopaedic pelvic trauma surgery, the dissertation includes work encompassing: (i) a series of statistical models to model shape and pose variations of one or more pelvic bones and an atlas of trajectory annotations; (ii) frameworks for automatic segmentation via registration of the statistical models to preoperative CT and planning of fixation trajectories and dislocation / fracture reduction; and (iii) 3D-2D guidance using intraoperative fluoroscopy. For intracranial neurosurgery, the dissertation includes three inter-modality deformable registrations using physic-based Demons and deep learning models for CT-guided and CBCT-guided procedures

    Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

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    In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

    Patient-Specific Implants in Musculoskeletal (Orthopedic) Surgery

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    Most of the treatments in medicine are patient specific, aren’t they? So why should we bother with individualizing implants if we adapt our therapy to patients anyway? Looking at the neighboring field of oncologic treatment, you would not question the fact that individualization of tumor therapy with personalized antibodies has led to the thriving of this field in terms of success in patient survival and positive responses to alternatives for conventional treatments. Regarding the latest cutting-edge developments in orthopedic surgery and biotechnology, including new imaging techniques and 3D-printing of bone substitutes as well as implants, we do have an armamentarium available to stimulate the race for innovation in medicine. This Special Issue of Journal of Personalized Medicine will gather all relevant new and developed techniques already in clinical practice. Examples include the developments in revision arthroplasty and tumor (pelvic replacement) surgery to recreate individual defects, individualized implants for primary arthroplasty to establish physiological joint kinematics, and personalized implants in fracture treatment, to name but a few

    Apprentissage de représentations pour la classification d’images biomédicales

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    Résumé La disponibilité croissante d'images médicales ouvre la porte à de nombreuses applications cliniques qui ont une incidence sur la prise en charge du patient. De nouveaux traits caractéristiques cliniquement pertinents peuvent alors être découverts pour expliquer, décrire et représenter une maladie. Les algorithmes traditionnels qui se basent sur des règles d'association manuellement construits font souvent défaut dans le domaine biomédical à cause de leur incapacité à capturer la forte variabilité au sein des données. L'apprentissage de représentations apprend plusieurs niveaux de représentations pour mieux capturer les facteurs de variation des données. L'hypothèse du projet de recherche du mémoire est que la classification par apprentissage de représentations apportera une information supplémentaire au médecin afin de l'aider dans son processus de décision. L'objectif principal, qui en découle, vise à étudier la faisabilité de l'apprentissage de représentations pour le milieu médical en vue de découvrir des structures cliniquement pertinentes au sein des données.Dans un premier temps, un algorithme d'apprentissage non-supervisé extrait des traits caractéristiques discriminants des déformations de la colonne vertébrale de patients atteints de la scoliose idiopathique de l'adolescent qui nécessitent une intervention chirurgicale. Le sous-objectif consiste à proposer une alternative aux systèmes de classification existants qui décrivent les déformations seulement selon deux plans alors que la scoliose déforme le rachis dans les trois dimensions de l'espace. Une large base de données a été rassemblée, composée de 915 reconstructions de la colonne vertébrale issues de 663 patients. Des auto-encodeurs empilés apprennent une représentation latente de ces reconstructions. Cette représentation de plus faible dimension démêle les facteurs de variation. Des sous-groupes sont par la suite formés par un algorithme de k-moyennes++. Onze sous-groupes statistiquement significatifs sont alors proposés pour expliquer la répartition de la déformation de la colonne vertébrale. Dans un second temps, un algorithme d'apprentissage supervisé extrait des traits caractéristiques discriminants au sein d'images médicales. Le sous-objectif consiste à classifier chaque voxel de l'image afin de produire une segmentation des reins. Une large base de données a été rassemblée, composée de 79 images tomographiques avec agent de contraste issues de 63 patients avec de nombreuses complications rénales. Un réseau à convolution est entrainé sur des patches de ces images pour apprendre des représentations discriminantes. Par la suite, des modifications sont appliquées à l'architecture, sans modifier les paramètres appris, pour produire les segmentations des reins. Les résultats obtenus permettent d'atteindre des scores élevés selon les métriques utilisées pour évaluer les segmentations en un court délai de calcul. Des coefficients de Dice de 94,35% pour le rein gauche et 93,07% pour le rein droit ont été atteints.Les résultats du mémoire offrent de nouvelles perspectives pour les pathologies abordées. L'application de l'apprentissage de représentations dans le domaine biomédical montre de nombreuses opportunités pour d'autres tâches à condition de rassembler une base de données d'une taille suffisante.----------Abstract The growing accessibility of medical imaging provides new clinical applications for patient care. New clinically relevant features can now be discovered to understand, describe and represent a disease. Traditional algorithms based on hand-engineered features usually fail in biomedical applications because of their lack of ability to capture the high variability in the data. Representation learning, often called deep learning, tackles this challenge by learning multiple levels of representation. The hypothesis of this master's thesis is that representation learning for biomedical image classification will yield additional information for the physician in his decision-making process. Therefore, the main objective is to assess the feasibility of representation learning for two different biomedical applications in order to learn clinically relevant structures within the data. First, a non-supervised learning algorithm extracts discriminant features to describe spine deformities that require a surgical intervention in patients with adolescent idiopathic scoliosis. The sub-objective is to propose an alternative to existing scoliosis classifications that only characterize spine deformities in 2D whereas a scoliotic is often deformed in 3D. 915 spine reconstructions from 663 patients were collected. Stacked auto-encoders learn a hidden representation of these reconstructions. This low-dimensional representation disentangles the main factors of variation in the geometrical appearance of spinal deformities. Sub-groups are clustered with the k-means++ algorithm. Eleven statistically significant sub-groups are extracted to explain how the different deformations of a scoliotic spine are distributed. Secondly, a supervised learning algorithm extracts discriminant features in medical images. The sub-objective is to classify every voxel in the image in order to produce kidney segmentations. 79 contrast-enhanced CT scans from 63 patients with renal complications were collected. A convolutional network is trained on a patch-based training scheme. Simple modifications to the architecture of the network, without modifying the parameters, compute the kidney segmentations on the whole image in a small amount of time. Results show high scores on the metrics used to assess the segmentations. Dice scores are 94.35% for the left kidney and 93.07% for the right kidney. The results show new perspectives for the diseases addressed in this master's thesis. Representation learning algorithms exhibit new opportunities for an application in other biomedical tasks as long as enough observations are available
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