Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Physical and statistical shape modelling in craniomaxillofacial surgery: a personalised approach for outcome prediction

Orthognathic surgery involves repositioning of the jaw bones to restore face function and shape for patients who require an operation as a result of a syndrome, due to growth disturbances in childhood or after trauma. As part of the preoperative assessment, three-dimensional medical imaging and computer-assisted surgical planning help to improve outcomes, and save time and cost. Computer-assisted surgical planning involves visualisation and manipulation of the patient anatomy and can be used to aid objective diagnosis, patient communication, outcome evaluation, and surgical simulation. Despite the benefits, the adoption of three-dimensional tools has remained limited beyond specialised hospitals and traditional two-dimensional cephalometric analysis is still the gold standard. This thesis presents a multidisciplinary approach to innovative surgical simulation involving clinical patient data, medical image analysis, engineering principles, and state-of-the-art machine learning and computer vision algorithms. Two novel three-dimensional computational models were developed to overcome the limitations of current computer-assisted surgical planning tools. First, a physical modelling approach – based on a probabilistic finite element model – provided patient-specific simulations and, through training and validation, population-specific parameters. The probabilistic model was equally accurate compared to two commercial programs whilst giving additional information regarding uncertainties relating to the material properties and the mismatch in bone position between planning and surgery. Second, a statistical modelling approach was developed that presents a paradigm shift in its modelling formulation and use. Specifically, a 3D morphable model was constructed from 5,000 non-patient and orthognathic patient faces for fully-automated diagnosis and surgical planning. Contrary to traditional physical models that are limited to a finite number of tests, the statistical model employs machine learning algorithms to provide the surgeon with a goal-driven patient-specific surgical plan. The findings in this thesis provide markers for future translational research and may accelerate the adoption of the next generation surgical planning tools to further supplement the clinical decision-making process and ultimately to improve patients’ quality of life

3D statistical shape analysis of the face in Apert syndrome

Timely diagnosis of craniofacial syndromes as well as adequate timing and choice of surgical technique are essential for proper care management. Statistical shape models and machine learning approaches are playing an increasing role in Medicine and have proven its usefulness. Frameworks that automate processes have become more popular. The use of 2D photographs for automated syndromic identification has shown its potential with the Face2Gene application. Yet, using 3D shape information without texture has not been studied in such depth. Moreover, the use of these models to understand shape change during growth and its applicability for surgical outcome measurements have not been analysed at length. This thesis presents a framework using state-of-the-art machine learning and computer vision algorithms to explore possibilities for automated syndrome identification based on shape information only. The purpose of this was to enhance understanding of the natural development of the Apert syndromic face and its abnormality as compared to a normative group. An additional method was used to objectify changes as result of facial bipartition distraction, a common surgical correction technique, providing information on the successfulness and on inadequacies in terms of facial normalisation. Growth curves were constructed to further quantify facial abnormalities in Apert syndrome over time along with 3D shape models for intuitive visualisation of the shape variations. Post-operative models were built and compared with age-matched normative data to understand where normalisation is coming short. The findings in this thesis provide markers for future translational research and may accelerate the adoption of the next generation diagnostics and surgical planning tools to further supplement the clinical decision-making process and ultimately to improve patients’ quality of life

Automatic image analysis of C-arm Computed Tomography images for ankle joint surgeries

Open reduction and internal fixation is a standard procedure in ankle surgery for treating a fractured fibula. Since fibula fractures are often accompanied by an injury of the syndesmosis complex, it is essential to restore the correct relative pose of the fibula relative to the adjoining tibia for the ligaments to heal. Otherwise, the patient might experience instability of the ankle leading to arthritis and ankle pain and ultimately revision surgery. Incorrect positioning referred to as malreduction of the fibula is assumed to be one of the major causes of unsuccessful ankle surgery. 3D C-arm imaging is the current standard procedure for revealing malreduction of fractures in the operating room. However, intra-operative visual inspection of the reduction result is complicated due to high inter-individual variation of the ankle anatomy and rather based on the subjective experience of the surgeon. A contralateral side comparison with the patient’s uninjured ankle is recommended but has not been integrated into clinical routine due to the high level of radiation exposure it incurs. This thesis presents the first approach towards a computer-assisted intra-operative contralateral side comparison of the ankle joint. The focus of this thesis was the design, development and validation of a software-based prototype for a fully automatic intra-operative assistance system for orthopedic surgeons. The implementation does not require an additional 3D C-arm scan of the uninjured ankle, thus reducing time consumption and cumulative radiation dose. A 3D statistical shape model (SSM) is used to reconstruct a 3D surface model from three 2D fluoroscopic projections representing the uninjured ankle. To this end, a 3D SSM segmentation is performed on the 3D image of the injured ankle to gain prior knowledge of the ankle. A 3D convolutional neural network (CNN) based initialization method was developed and its outcome was incorporated into the SSM adaption step. Segmentation quality was shown to be improved in terms of accuracy and robustness compared to the pure intensity-based SSM. This allows us to overcome the limitations of the previously proposed methods, namely inaccuracy due to metal artifacts and the lack of device-to-patient orientation of the C-arm. A 2D-CNN is employed to extract semantic knowledge from all fluoroscopic projection images. This step of the pipeline both creates features for the subsequent reconstruction and also helps to pre-initialize the 3D-SSM without user interaction. A 2D-3D multi-bone reconstruction method has been developed which uses distance maps of the 2D features for fast and accurate correspondence optimization and SSM adaption. This is the central and most crucial component of the workflow. This is the first time that a bone reconstruction method has been applied to the complex ankle joint and the first reconstruction method using CNN based segmentations as features. The reconstructed 3D-SSM of the uninjured ankle can be back-projected and visualized in a workflow-oriented manner to procure clear visualization of the region of interest, which is essential for the evaluation of the reduction result. The surgeon can thus directly compare an overlay of the contralateral ankle with the injured ankle. The developed methods were evaluated individually using data sets acquired during a cadaver study and representative clinical data acquired during fibular reduction. A hierarchical evaluation was designed to assess the inaccuracies of the system on different levels and to identify major sources of error. The overall evaluation performed on eleven challenging clinical datasets acquired for manual contralateral side comparison showed that the system is capable of accurately reconstructing 3D surface models of the uninjured ankle solely using three projection images. A mean Hausdorff distance of 1.72 mm was measured when comparing the reconstruction result to the ground truth segmentation and almost achieved the high required clinical accuracy of 1-2 mm. The overall error of the pipeline was mainly attributed to inaccuracies in the 2D-CNN segmentation. The consistency of these results requires further validation on a larger dataset. The workflow proposed in this thesis establishes the first approach to enable automatic computer-assisted contralateral side comparison in ankle surgery. The feasibility of the proposed approach was proven on a limited amount of clinical cases and has already yielded good results. The next important step is to alleviate the identified bottlenecks in the approach by providing more training data in order to further improve the accuracy. In conclusion, the new approach presented gives the chance to guide the surgeon during the reduction process, improve the surgical outcome while avoiding additional radiation exposure and reduce the number of revision surgeries in the long term

Tenfold your photons -- a physically-sound approach to filtering-based variance reduction of Monte-Carlo-simulated dose distributions

X-ray dose constantly gains interest in the interventional suite. With dose being generally difficult to monitor reliably, fast computational methods are desirable. A major drawback of the gold standard based on Monte Carlo (MC) methods is its computational complexity. Besides common variance reduction techniques, filter approaches are often applied to achieve conclusive results within a fraction of time. Inspired by these methods, we propose a novel approach. We down-sample the target volume based on the fraction of mass, simulate the imaging situation, and then revert the down-sampling. To this end, the dose is weighted by the mass energy absorption, up-sampled, and distributed using a guided filter. Eventually, the weighting is inverted resulting in accurate high resolution dose distributions. The approach has the potential to considerably speed-up MC simulations since less photons and boundary checks are necessary. First experiments substantiate these assumptions. We achieve a median accuracy of 96.7 % to 97.4 % of the dose estimation with the proposed method and a down-sampling factor of 8 and 4, respectively. While maintaining a high accuracy, the proposed method provides for a tenfold speed-up. The overall findings suggest the conclusion that the proposed method has the potential to allow for further efficiency.Comment: 6 pages, 3 figures, Bildverarbeitung f\"ur die Medizin 202

Landmark Localization, Feature Matching and Biomarker Discovery from Magnetic Resonance Images

The work presented in this thesis proposes several methods that can be roughly divided into three different categories: I) landmark localization in medical images, II) feature matching for image registration, and III) biomarker discovery in neuroimaging. The first part deals with the identification of anatomical landmarks. The motivation stems from the fact that the manual identification and labeling of these landmarks is very time consuming and prone to observer errors, especially when large datasets must be analyzed. In this thesis we present three methods to tackle this challenge: A landmark descriptor based on local self-similarities (SS), a subspace building framework based on manifold learning and a sparse coding landmark descriptor based on data-specific learned dictionary basis. The second part of this thesis deals with finding matching features between a pair of images. These matches can be used to perform a registration between them. Registration is a powerful tool that allows mapping images in a common space in order to aid in their analysis. Accurate registration can be challenging to achieve using intensity based registration algorithms. Here, a framework is proposed for learning correspondences in pairs of images by matching SS features and random sample and consensus (RANSAC) is employed as a robust model estimator to learn a deformation model based on feature matches. Finally, the third part of the thesis deals with biomarker discovery using machine learning. In this section a framework for feature extraction from learned low-dimensional subspaces that represent inter-subject variability is proposed. The manifold subspace is built using data-driven regions of interest (ROI). These regions are learned via sparse regression, with stability selection. Also, probabilistic distribution models for different stages in the disease trajectory are estimated for different class populations in the low-dimensional manifold and used to construct a probabilistic scoring function.Open Acces

Segmentation of pelvic structures from preoperative images for surgical planning and guidance

Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces

Deep Learning-based Solutions to Improve Diagnosis in Wireless Capsule Endoscopy

[eng] Deep Learning (DL) models have gained extensive attention due to their remarkable performance in a wide range of real-world applications, particularly in computer vision. This achievement, combined with the increase in available medical records, has made it possible to open up new opportunities for analyzing and interpreting healthcare data. This symbiotic relationship can enhance the diagnostic process by identifying abnormalities, patterns, and trends, resulting in more precise, personalized, and effective healthcare for patients. Wireless Capsule Endoscopy (WCE) is a non-invasive medical imaging technique used to visualize the entire Gastrointestinal (GI) tract. Up to this moment, physicians meticulously review the captured frames to identify pathologies and diagnose patients. This manual process is time- consuming and prone to errors due to the challenges of interpreting the complex nature of WCE procedures. Thus, it demands a high level of attention, expertise, and experience. To overcome these drawbacks, shorten the screening process, and improve the diagnosis, efficient and accurate DL methods are required. This thesis proposes DL solutions to the following problems encountered in the analysis of WCE studies: pathology detection, anatomical landmark identification, and Out-of-Distribution (OOD) sample handling. These solutions aim to achieve robust systems that minimize the duration of the video analysis and reduce the number of undetected lesions. Throughout their development, several DL drawbacks have appeared, including small and imbalanced datasets. These limitations have also been addressed, ensuring that they do not hinder the generalization of neural networks, leading to suboptimal performance and overfitting. To address the previous WCE problems and overcome the DL challenges, the proposed systems adopt various strategies that utilize the power advantage of Triplet Loss (TL) and Self-Supervised Learning (SSL) techniques. Mainly, TL has been used to improve the generalization of the models, while SSL methods have been employed to leverage the unlabeled data to obtain useful representations. The presented methods achieve State-of-the-art results in the aforementioned medical problems and contribute to the ongoing research to improve the diagnostic of WCE studies.[cat] Els models d’aprenentatge profund (AP) han acaparat molta atenció a causa del seu rendiment en una àmplia gamma d'aplicacions del món real, especialment en visió per ordinador. Aquest fet, combinat amb l'increment de registres mèdics disponibles, ha permès obrir noves oportunitats per analitzar i interpretar les dades sanitàries. Aquesta relació simbiòtica pot millorar el procés de diagnòstic identificant anomalies, patrons i tendències, amb la conseqüent obtenció de diagnòstics sanitaris més precisos, personalitzats i eficients per als pacients. La Capsula endoscòpica (WCE) és una tècnica d'imatge mèdica no invasiva utilitzada per visualitzar tot el tracte gastrointestinal (GI). Fins ara, els metges revisen minuciosament els fotogrames capturats per identificar patologies i diagnosticar pacients. Aquest procés manual requereix temps i és propens a errors. Per tant, exigeix un alt nivell d'atenció, experiència i especialització. Per superar aquests inconvenients, reduir la durada del procés de detecció i millorar el diagnòstic, es requereixen mètodes eficients i precisos d’AP. Aquesta tesi proposa solucions que utilitzen AP per als següents problemes trobats en l'anàlisi dels estudis de WCE: detecció de patologies, identificació de punts de referència anatòmics i gestió de mostres que pertanyen fora del domini. Aquestes solucions tenen com a objectiu aconseguir sistemes robustos que minimitzin la durada de l'anàlisi del vídeo i redueixin el nombre de lesions no detectades. Durant el seu desenvolupament, han sorgit diversos inconvenients relacionats amb l’AP, com ara conjunts de dades petits i desequilibrats. Aquestes limitacions també s'han abordat per assegurar que no obstaculitzin la generalització de les xarxes neuronals, evitant un rendiment subòptim. Per abordar els problemes anteriors de WCE i superar els reptes d’AP, els sistemes proposats adopten diverses estratègies que aprofiten l'avantatge de la Triplet Loss (TL) i les tècniques d’auto-aprenentatge. Principalment, s'ha utilitzat TL per millorar la generalització dels models, mentre que els mètodes d’autoaprenentatge s'han emprat per aprofitar les dades sense etiquetar i obtenir representacions útils. Els mètodes presentats aconsegueixen bons resultats en els problemes mèdics esmentats i contribueixen a la investigació en curs per millorar el diagnòstic dels estudis de WCE

Visualizing and Predicting the Effects of Rheumatoid Arthritis on Hands

This dissertation was inspired by difficult decisions patients of chronic diseases have to make about about treatment options in light of uncertainty. We look at rheumatoid arthritis (RA), a chronic, autoimmune disease that primarily affects the synovial joints of the hands and causes pain and deformities. In this work, we focus on several parts of a computer-based decision tool that patients can interact with using gestures, ask questions about the disease, and visualize possible futures. We propose a hand gesture based interaction method that is easily setup in a doctor\u27s office and can be trained using a custom set of gestures that are least painful. Our system is versatile and can be used for operations like simple selections to navigating a 3D world. We propose a point distribution model (PDM) that is capable of modeling hand deformities that occur due to RA and a generalized fitting method for use on radiographs of hands. Using our shape model, we show novel visualization of disease progression. Using expertly staged radiographs, we propose a novel distance metric learning and embedding technique that can be used to automatically stage an unlabeled radiograph. Given a large set of expertly labeled radiographs, our data-driven approach can be used to extract different modes of deformation specific to a disease