2,156 research outputs found
Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction
Various deep learning models have been proposed for 3D bone shape
reconstruction from two orthogonal (biplanar) X-ray images. However, it is
unclear how these models compare against each other since they are evaluated on
different anatomy, cohort and (often privately held) datasets. Moreover, the
impact of the commonly optimized image-based segmentation metrics such as dice
score on the estimation of clinical parameters relevant in 2D-3D bone shape
reconstruction is not well known. To move closer toward clinical translation,
we propose a benchmarking framework that evaluates tasks relevant to real-world
clinical scenarios, including reconstruction of fractured bones, bones with
implants, robustness to population shift, and error in estimating clinical
parameters. Our open-source platform provides reference implementations of 8
models (many of whose implementations were not publicly available), APIs to
easily collect and preprocess 6 public datasets, and the implementation of
automatic clinical parameter and landmark extraction methods. We present an
extensive evaluation of 8 2D-3D models on equal footing using 6 public datasets
comprising images for four different anatomies. Our results show that
attention-based methods that capture global spatial relationships tend to
perform better across all anatomies and datasets; performance on clinically
relevant subgroups may be overestimated without disaggregated reporting; ribs
are substantially more difficult to reconstruct compared to femur, hip and
spine; and the dice score improvement does not always bring a corresponding
improvement in the automatic estimation of clinically relevant parameters.Comment: accepted to NeurIPS 202
Artificial Intelligence Techniques in Medical Imaging: A Systematic Review
This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts
Unveiling healthcare data archiving: Exploring the role of artificial intelligence in medical image analysis
Gli archivi sanitari digitali possono essere considerati dei moderni database progettati per immagazzinare e gestire ingenti quantitaÌ di informazioni mediche, dalle cartelle cliniche dei pazienti, a studi clinici fino alle immagini mediche e a dati genomici. I dati strutturati e non strutturati che compongono gli archivi sanitari sono oggetto di scrupolose e rigorose procedure di validazione per garantire accuratezza, affidabilitaÌ e standardizzazione a fini clinici e di ricerca.
Nel contesto di un settore sanitario in continua e rapida evoluzione, lâintelligenza artificiale (IA) si propone come una forza trasformativa, capace di riformare gli archivi sanitari digitali migliorando la gestione, lâanalisi e il recupero di vasti set di dati clinici, al fine di ottenere decisioni cliniche piuÌ informate e ripetibili, interventi tempestivi e risultati migliorati per i pazienti.
Tra i diversi dati archiviati, la gestione e lâanalisi delle immagini mediche in archivi digitali presentano numerose sfide dovute allâeterogeneitaÌ dei dati, alla variabilitaÌ della qualitaÌ delle immagini, noncheÌ alla mancanza di annotazioni. Lâimpiego di soluzioni basate sullâIA puoÌ aiutare a risolvere efficacemente queste problematiche, migliorando lâaccuratezza dellâanalisi delle immagini, standardizzando la qualitaÌ dei dati e facilitando la generazione di annotazioni dettagliate.
Questa tesi ha lo scopo di utilizzare algoritmi di IA per lâanalisi di immagini mediche depositate in archivi sanitari digitali. Il presente lavoro propone di indagare varie tecniche di imaging medico, ognuna delle quali eÌ caratterizzata da uno specifico dominio di applicazione e presenta quindi un insieme unico di sfide, requisiti e potenziali esiti. In particolare, in questo lavoro di tesi saraÌ oggetto di approfondimento lâassistenza diagnostica degli algoritmi di IA per tre diverse tecniche di imaging, in specifici scenari clinici:
i) Immagini endoscopiche ottenute durante esami di laringoscopia; cioÌ include unâesplorazione approfondita di tecniche come la detection di keypoints per la stima della motilitaÌ delle corde vocali e la segmentazione di tumori del tratto aerodigestivo superiore;
ii) Immagini di risonanza magnetica per la segmentazione dei dischi intervertebrali, per la diagnosi e il trattamento di malattie spinali, cosiÌ come per lo svolgimento di interventi chirurgici guidati da immagini;
iii) Immagini ecografiche in ambito reumatologico, per la valutazione della sindrome del tunnel carpale attraverso la segmentazione del nervo mediano.
Le metodologie esposte in questo lavoro evidenziano lâefficacia degli algoritmi di IA nellâanalizzare immagini mediche archiviate. I progressi metodologici ottenuti sottolineano il notevole potenziale dellâIA nel rivelare informazioni implicitamente presenti negli archivi sanitari digitali
Image-based biomechanical models of the musculoskeletal system
Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described
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