387 research outputs found

    Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site data

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    Many clinical and research studies of the human brain require accurate structural MRI segmentation. While traditional atlas-based methods can be applied to volumes from any acquisition site, recent deep learning algorithms ensure high accuracy only when tested on data from the same sites exploited in training (i.e., internal data). Performance degradation experienced on external data (i.e., unseen volumes from unseen sites) is due to the inter-site variability in intensity distributions, and to unique artefacts caused by different MR scanner models and acquisition parameters. To mitigate this site-dependency, often referred to as the scanner effect, we propose LOD-Brain, a 3D convolutional neural network with progressive levels-of-detail (LOD), able to segment brain data from any site. Coarser network levels are responsible for learning a robust anatomical prior helpful in identifying brain structures and their locations, while finer levels refine the model to handle site-specific intensity distributions and anatomical variations. We ensure robustness across sites by training the model on an unprecedentedly rich dataset aggregating data from open repositories: almost 27,000 T1w volumes from around 160 acquisition sites, at 1.5 - 3T, from a population spanning from 8 to 90 years old. Extensive tests demonstrate that LOD-Brain produces state-of-the-art results, with no significant difference in performance between internal and external sites, and robust to challenging anatomical variations. Its portability paves the way for large-scale applications across different healthcare institutions, patient populations, and imaging technology manufacturers. Code, model, and demo are available on the project website

    Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning

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    Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and direct surgical procedures, and to track the development of bone-related diseases. This often involves radiologists who have to annotate bones manually or in a semi-automatic way, which is a time consuming task. Their annotation workload can be reduced by automated segmentation and detection of individual bones. This automation of distinct bone segmentation not only has the potential to accelerate current workflows but also opens up new possibilities for processing and presenting medical data for planning, navigation, and education. In this thesis, we explored the use of deep learning for automating the segmentation of all individual bones within an upper-body CT scan. To do so, we had to find a network architec- ture that provides a good trade-off between the problem’s high computational demands and the results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out to eliminate the most prevalent types of error. To do so, we introduced an novel method called binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin- guishing bone from non-bone is conducted separately from identifying the individual bones. Both predictions are then merged, which leads to superior results. Another type of error is tack- led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input into the network while keeping the growth of additional pixels in check. Overall, we present a deep-learning-based method that reliably segments most of the over one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter quickly enough to be used in interactive software. Our algorithm has been included in our groups virtual reality medical image visualisation software SpectoVR with the plan to be used as one of the puzzle piece in surgical planning and navigation, as well as in the education of future doctors

    Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images

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    Graph convolutional neural networks have shown significant potential in natural and histopathology images. However, their use has only been studied in a single magnification or multi-magnification with late fusion. In order to leverage the multi-magnification information and early fusion with graph convolutional networks, we handle different embedding spaces at each magnification by introducing the Multi-Scale Relational Graph Convolutional Network (MS-RGCN) as a multiple instance learning method. We model histopathology image patches and their relation with neighboring patches and patches at other scales (i.e., magnifications) as a graph. To pass the information between different magnification embedding spaces, we define separate message-passing neural networks based on the node and edge type. We experiment on prostate cancer histopathology images to predict the grade groups based on the extracted features from patches. We also compare our MS-RGCN with multiple state-of-the-art methods with evaluations on several source and held-out datasets. Our method outperforms the state-of-the-art on all of the datasets and image types consisting of tissue microarrays, whole-mount slide regions, and whole-slide images. Through an ablation study, we test and show the value of the pertinent design features of the MS-RGCN

    Picture Perfect: The Status of Image Quality in Prostate MRI

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    Magnetic resonance imaging is the gold standard imaging modality for the diagnosis of prostate cancer (PCa). Image quality is a fundamental prerequisite for the ability to detect clinically significant disease. In this critical review, we separate the issue of image quality into quality improvement and quality assessment. Beginning with the evolution of technical recommendations for scan acquisition, we investigate the role of patient preparation, scanner factors, and more advanced sequences, including those featuring Artificial Intelligence (AI), in determining image quality. As means of quality appraisal, the published literature on scoring systems (including the Prostate Imaging Quality score), is evaluated. Finally, the application of AI and teaching courses as ways to facilitate quality assessment are discussed, encouraging the implementation of future image quality initiatives along the PCa diagnostic and monitoring pathway. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3

    Personalized prostate cancer management : AI-assisted prostate pathology and improved active surveillance

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    Prostate cancer is a major global health concern and is the most common cancer-related cause of death in Sweden. Prostate cancer screening using PSA has been shown to reduce prostate cancer mortality but also leads to significant overdiagnosis and overtreatment of low-risk cancers. Improved risk stratification and effective active surveillance are crucial to balancing the benefits of screening with the risk of overdiagnosis and overtreatment. In Study I, we studied the uptake and the follow-up of active surveillance using a retrospective cohort of patients who were diagnosed with low-risk prostate cancer between 2008 and 2017 in Stockholm County. Our results showed that only 50% of eligible active surveillance patients received active surveillance as their primary treatment choice at diagnosis. Most men that enrolled in active surveillance remained on surveillance during the first years after diagnosis (82% during a median 3.5 years), but did not receive a follow up according to guidelines with regard to repeat biopsies and PSA tests. Current clinical practice has seen an increase in the use of magnetic resonance imaging (MRI) and the incorporation of risk prediction models to select men with the highest suspicion of clinically significant prostate cancer for prostate biopsy. However, the effectiveness and how MRI and risk prediction models should be incorporated into active surveillance follow-up have yet to be established. Study II evaluated the performance of MRI-targeted biopsies and a blood-based risk prediction model (the Stockholm3 test) for monitoring disease progression in patients on active surveillance and compared this to the conventional follow-up using PSA and systematic biopsies. When MRI-targeted and systematic biopsies were combined, the detection rate of clinically significant prostate cancer increased when compared to conventional systematic biopsies. Biopsies performed in MRI-positive men resulted in a 49% reduction in performed biopsies, at the expense of failing to diagnose 1.4% clinically significant prostate cancer in MRInegative men. The incorporation of the Stockholm3 test showed a 27% reduction in required MRI investigations and a 57% reduction in performed biopsies compared to performing only systematic biopsies. In Study III, we digitized biopsy cores from STHLM3 participants to develop an artificial intelligence (AI) for prostate cancer diagnostics. The AI system demonstrated clinically useful performance that was comparable to that of the study pathologist for cancer detection (AUC of 0.986) and for predictions of cancer length (correlation of 0.87) and grading performance that was on par with that of expert prostate pathologists. In Study IV, we developed a conformal predictor to estimate the uncertainty of the predictions for the model in Study III. The uncertainty estimates were used to control the error rate so that only predictions with high confidence are accepted and unreliable predictions can be detected. The conformal predictor was able to identify unreliable predictions as a result of variations in digital pathology scanners, preparation of tissue in different pathology laboratories, and the existence of unusual prostate tissue that the AI model was not exposed to during training. Little is known about the relationships between prostate cancer genetic risk factors and the morphology of prostate tissue. In Study V:, we investigated whether weakly supervised deep learning can learn to detect such possible associations. The findings in this paper imply relationships between prostatic tissue morphology and genetic risk factors for prostate cancer, particularly in young men. These results provide proof of principle for exploring the use of morphological information in multi-modal prostate cancer risk prediction algorithms. In conclusion, the purpose of this thesis was to describe possible extensions to improve prostate cancer active surveillance management, as well as to develop prediction models for improved prostate cancer diagnostics

    Automatic classification of prostate cancer Gleason scores from biparametric MRI using deep convolutional neural networks

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    Prostate cancer is one of the most common types of cancer in the world. To reduce the number of deaths caused by it, effective diagnostic methods are of paramount importance to detect the clinically significant cases early enough. The current diagnostic protocols include, among other methods, magnetic resonance imaging which can be used to assess whether a patient suffers from prostate cancer and whether the possible cancer lesions are clinically significant. However, the images are difficult to interpret, and thus the inter-reader reliability is not very good. To address this problem, in this thesis machine learning models are trained to automatically segment and classify prostate cancer lesions from magnetic resonance images. The problem proved to be difficult even for computers, at least with the relatively small data set size. The highest Dice similarity coefficients for the used Gleason score groups approached 0.4, which is not enough to replace the work of professionals or even provide meaningful help for doctors. In conclusion, the task of automatic segmentation and classification of prostate cancer lesions remains an open problem. Improving the performance to a useful level would likely require a noticeably larger dataset or at least a model that better incorporates the knowledge of the trained professionals

    Anwendungen maschinellen Lernens für datengetriebene Prävention auf Populationsebene

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    Healthcare costs are systematically rising, and current therapy-focused healthcare systems are not sustainable in the long run. While disease prevention is a viable instrument for reducing costs and suffering, it requires risk modeling to stratify populations, identify high- risk individuals and enable personalized interventions. In current clinical practice, however, systematic risk stratification is limited: on the one hand, for the vast majority of endpoints, no risk models exist. On the other hand, available models focus on predicting a single disease at a time, rendering predictor collection burdensome. At the same time, the den- sity of individual patient data is constantly increasing. Especially complex data modalities, such as -omics measurements or images, may contain systemic information on future health trajectories relevant for multiple endpoints simultaneously. However, to date, this data is inaccessible for risk modeling as no dedicated methods exist to extract clinically relevant information. This study built on recent advances in machine learning to investigate the ap- plicability of four distinct data modalities not yet leveraged for risk modeling in primary prevention. For each data modality, a neural network-based survival model was developed to extract predictive information, scrutinize performance gains over commonly collected covariates, and pinpoint potential clinical utility. Notably, the developed methodology was able to integrate polygenic risk scores for cardiovascular prevention, outperforming existing approaches and identifying benefiting subpopulations. Investigating NMR metabolomics, the developed methodology allowed the prediction of future disease onset for many common diseases at once, indicating potential applicability as a drop-in replacement for commonly collected covariates. Extending the methodology to phenome-wide risk modeling, elec- tronic health records were found to be a general source of predictive information with high systemic relevance for thousands of endpoints. Assessing retinal fundus photographs, the developed methodology identified diseases where retinal information most impacted health trajectories. In summary, the results demonstrate the capability of neural survival models to integrate complex data modalities for multi-disease risk modeling in primary prevention and illustrate the tremendous potential of machine learning models to disrupt medical practice toward data-driven prevention at population scale.Die Kosten im Gesundheitswesen steigen systematisch und derzeitige therapieorientierte Gesundheitssysteme sind nicht nachhaltig. Angesichts vieler verhinderbarer Krankheiten stellt die Prävention ein veritables Instrument zur Verringerung von Kosten und Leiden dar. Risikostratifizierung ist die grundlegende Voraussetzung für ein präventionszentri- ertes Gesundheitswesen um Personen mit hohem Risiko zu identifizieren und Maßnah- men einzuleiten. Heute ist eine systematische Risikostratifizierung jedoch nur begrenzt möglich, da für die meisten Krankheiten keine Risikomodelle existieren und sich verfüg- bare Modelle auf einzelne Krankheiten beschränken. Weil für deren Berechnung jeweils spezielle Sets an Prädiktoren zu erheben sind werden in Praxis oft nur wenige Modelle angewandt. Gleichzeitig versprechen komplexe Datenmodalitäten, wie Bilder oder -omics- Messungen, systemische Informationen über zukünftige Gesundheitsverläufe, mit poten- tieller Relevanz für viele Endpunkte gleichzeitig. Da es an dedizierten Methoden zur Ex- traktion klinisch relevanter Informationen fehlt, sind diese Daten jedoch für die Risikomod- ellierung unzugänglich, und ihr Potenzial blieb bislang unbewertet. Diese Studie nutzt ma- chinelles Lernen, um die Anwendbarkeit von vier Datenmodalitäten in der Primärpräven- tion zu untersuchen: polygene Risikoscores für die kardiovaskuläre Prävention, NMR Meta- bolomicsdaten, elektronische Gesundheitsakten und Netzhautfundusfotos. Pro Datenmodal- ität wurde ein neuronales Risikomodell entwickelt, um relevante Informationen zu extra- hieren, additive Information gegenüber üblicherweise erfassten Kovariaten zu quantifizieren und den potenziellen klinischen Nutzen der Datenmodalität zu ermitteln. Die entwickelte Me-thodik konnte polygene Risikoscores für die kardiovaskuläre Prävention integrieren. Im Falle der NMR-Metabolomik erschloss die entwickelte Methodik wertvolle Informa- tionen über den zukünftigen Ausbruch von Krankheiten. Unter Einsatz einer phänomen- weiten Risikomodellierung erwiesen sich elektronische Gesundheitsakten als Quelle prädik- tiver Information mit hoher systemischer Relevanz. Bei der Analyse von Fundusfotografien der Netzhaut wurden Krankheiten identifiziert für deren Vorhersage Netzhautinformationen genutzt werden könnten. Zusammengefasst zeigten die Ergebnisse das Potential neuronaler Risikomodelle die medizinische Praxis in Richtung einer datengesteuerten, präventionsori- entierten Medizin zu verändern

    The Use of Strategic Public Relations Communication Techniques in Campaigns to Raise Awareness of Breast Cancer: A Case Study of Breast Cancer Campaigns in Saudi Arabian Charities

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    This study investigates the efforts of cancer charities in the Kingdom of Saudi Arabia to raise awareness of breast cancer through communication campaign techniques in order to reduce its incidence, which has been rising in the Saudi population for several years. Applying the Diffusion of Innovations Theory of Rogers (2003) as a theoretical framework, qualitative primary data was collected through semi-structured interviews with 12 individuals working in public relations (PR) and communications practice at six cancer charities to understand their experience of designing and planning health communication strategies to bring about health-related behavioural change among Saudi women. The study also involved qualitative content analysis of the Twitter pages of the six charities during Breast Cancer Awareness Month (October) in 2018 to determine communicative functions in accordance with the classification scheme of Lovejoy and Saxton (2012). The interview data revealed that not all of the charities employed dedicated PR practitioners in their communication departments, but all carried out some PR functions, with a significant emphasis on the technical rather than managerial roles of PR. The participants were found to use various communication strategies and methods to reach different target audiences. However, considerable difficulty was experienced in the design of specific campaign planning strategies, with the participants demonstrating little use of breast cancer campaign strategy to overcome the lack of knowledge and awareness among Saudi women. The study confirmed that the charities did not use Twitter strategically, employing the platform largely as a one-way channel of information communication. Additionally, the charities rarely used promotional and mobilising messages as an action function and did not follow the commonly accepted relationship-building strategies such as dialogic and two-way communication

    Radiomics analyses for outcome prediction in patients with locally advanced rectal cancer and glioblastoma multiforme using multimodal imaging data

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    Personalized treatment strategies for oncological patient management can improve outcomes of patient populations with heterogeneous treatment response. The implementation of such a concept requires the identification of biomarkers that can precisely predict treatment outcome. In the context of this thesis, we develop and validate biomarkers from multimodal imaging data for the outcome prediction after treatment in patients with locally advanced rectal cancer (LARC) and in patients with newly diagnosed glioblastoma multiforme (GBM), using conventional feature-based radiomics and deep-learning (DL) based radiomics. For LARC patients, we identify promising radiomics signatures combining computed tomography (CT) and T2-weighted (T2-w) magnetic resonance imaging (MRI) with clinical parameters to predict tumour response to neoadjuvant chemoradiotherapy (nCRT). Further, the analyses of externally available radiomics models for LARC reveal a lack of reproducibility and the need for standardization of the radiomics process. For patients with GBM, we use postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w MRI for the detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS). We show that DL models built on MET-PET have an improved diagnostic and prognostic value as compared to MRI
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