39 research outputs found

    Domain Adaptation for Novel Imaging Modalities with Application to Prostate MRI

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    The need for training data can impede the adoption of novel imaging modalities for deep learning-based medical image analysis. Domain adaptation can mitigate this problem by exploiting training samples from an existing, densely-annotated source domain within a novel, sparsely-annotated target domain, by bridging the differences between the two domains. In this thesis we present methods for adapting between diffusion-weighed (DW)-MRI data from multiparametric (mp)-MRI acquisitions and VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors) MRI, a richer DW-MRI technique involving an optimized acquisition protocol for cancer characterization. We also show that the proposed methods are general and their applicability extends beyond medical imaging. First, we propose a semi-supervised domain adaptation method for prostate lesion segmentation on VERDICT MRI. Our approach relies on stochastic generative modelling to translate across two heterogeneous domains at pixel-space and exploits the inherent uncertainty in the cross-domain mapping to generate multiple outputs conditioned on a single input. We further extend this approach to the unsupervised scenario where there is no labeled data for the target domain. We rely on stochastic generative modelling to translate across the two domains at pixel space and introduce two loss functions that promote semantic consistency. Finally we demonstrate that the proposed approaches extend beyond medical image analysis and focus on unsupervised domain adaptation for semantic segmentation of urban scenes. We show that relying on stochastic generative modelling allows us to train more accurate target networks and achieve state-of-the-art performance on two challenging semantic segmentation benchmarks

    Contributions of biomechanical modeling and machine learning to the automatic registration of Multiparametric Magnetic Resonance and Transrectal Echography for prostate brachytherapy

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    El cáncer de próstata (CaP) es el primer cáncer por incidencia en hombres en países occidentales, y el tercero en mortalidad. Tras detectar en sangre una elevación del Antígeno Prostático Específico (PSA) o tras tacto rectal sospechoso se realiza una Resonancia Magnética (RM) de la próstata, que los radiólogos analizan para localizar las regiones sospechosas. A continuación, estas se biopsian, es decir, se toman muestras vivas que posteriormente serán analizadas histopatológicamente para confirmar la presencia de cáncer y establecer su grado de agresividad. Durante la biopsia se emplea típicamente Ultrasonidos (US) para el guiado y la localización de las lesiones. Sin embargo, estas no son directamente visibles en US, y el urólogo necesita usar software de fusión que realice un registro RM-US que transfiera la localizaciones marcadas en MR al US. Esto es fundamental para asegurar que las muestras tomadas provienen verdaderamente de la zona sospechosa. En este trabajo se compendian cinco publicaciones que emplean diversos algoritmos de Inteligencia Artificial (IA) para analizar las imágenes de próstata (RM y US) y con ello mejorar la eficiencia y precisión en el diagnóstico, biopsia y tratamiento del CaP: 1. Segmentación automática de próstata en RM y US: Segmentar la próstata consiste en delimitar o marcar la próstata en una imagen médica, separándola del resto de órganos o estructuras. Automatizar por completo esta tarea, que es previa a todo análisis posterior, permite ahorrar un tiempo significativo a radiólogos y urólogos, mejorando también la precisión y repetibilidad. 2. Mejora de la resolución de segmentación: Se presenta una metodología para mejorar la resolución de las segmentaciones anteriores. 3. Detección y clasificación automática de lesiones en RM: Se entrena un modelo basado en IA para detectar las lesiones como lo haría un radiólogo, asignándoles también una estimación del riesgo. Se logra mejorar la precisión diagnóstica, dando lugar a un sistema totalmente automático que podría implantarse para segunda opinión clínica o como criterio para priorización. 4. Simulación del comportamiento biomecánico en tiempo real: Se propone acelerar la simulación del comportamiento biomecánico de órganos blandos mediante el uso de IA. 5. Registro automático RM-US: El registro permite localizar en US las lesiones marcadas en RM. Una alta precisión en esta tarea es esencial para la corrección de la biopsia y/o del tratamiento focal del paciente (como braquiterapia de alta tasa). Se plantea el uso de la IA para resolver el problema de registro en tiempo casi real, utilizando modelos biomecánicos subyacentes.Prostate cancer (PCa) is the most common malignancy in western males, and third by mortality. After detecting elevated Prostate Specific Antigen (PSA) blood levels or after a suspicious rectal examination, a Magnetic Resonance (MR) image of the prostate is acquired and assessed by radiologists to locate suspicious regions. These are then biopsied, i.e. living tissue samples are collected and analyzed histopathologically to confirm the presence of cancer and establish its degree of aggressiveness. During the biopsy procedure, Ultrasound (US) is typically used for guidance and lesion localization. However, lesions are not directly visible in US, and the urologist needs to use fusion software to performs MR-US registration, so that the MR-marked locations can be transferred to the US image. This is essential to ensure that the collected samples truly come from the suspicious area. This work compiles five publications employing several Artificial Intelligence (AI) algorithms to analyze prostate images (MR and US) and thereby improve the efficiency and accuracy in diagnosis, biopsy and treatment of PCa: 1. Automatic prostate segmentation in MR and US: Prostate segmentation consists in delimiting or marking the prostate in a medical image, separating it from the rest of the organs or structures. Automating this task fully, which is required for any subsequent analysis, saves significant time for radiologists and urologists, while also improving accuracy and repeatability. 2. Segmentation resolution enhancement: A methodology for improving the resolution of the previously obtained segmentations is presented. 3. Automatic detection and classification of MR lesions: An AI model is trained to detect lesions as a radiologist would and to estimate their risk. The model achieves improved diagnostic accuracy, resulting in a fully automatic system that could be used as a second clinical opinion or as a criterion for patient prioritization. 4. Simulation of biomechanical behavior in real time: It is proposed to accelerate the simulation of biomechanical behavior of soft organs using AI. 5. Automatic MR-US registration: Registration allows localization of MR-marked lesions on US. High accuracy in this task is essential for the correctness of the biopsy and/or focal treatment procedures (such as high-rate brachytherapy). Here, AI is used to solve the registration problem in near-real time, while exploiting underlying biomechanically-compatible models

    Urological Cancer 2020

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    This Urological Cancer 2020 collection contains a set of multidisciplinary contributions to the extraordinary heterogeneity of tumor mechanisms, diagnostic approaches, and therapies of the renal, urinary tract, and prostate cancers, with the intention of offering to interested readers a representative snapshot of the status of urological research

    A review of artificial intelligence in prostate cancer detection on imaging

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    A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care

    Advanced Computational Methods for Oncological Image Analysis

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    [Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

    Medical Robotics

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    The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

    U-Net and its variants for medical image segmentation: theory and applications

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    U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.Comment: 42 pages, in IEEE Acces
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