1,067 research outputs found

    Modeling and MR-thermometry for adaptive hyperthermia in cervical Cancer

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    Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging

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    Is attention all you need in medical image analysis? A review

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    Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. The main disadvantage of typical CNN models is that they ignore global pixel relationships within images, which limits their generalisation ability to understand out-of-distribution data with different 'global' information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced a comprehensive analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated

    Characterising the neck motor system of the blowfly

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    Flying insects use visual, mechanosensory, and proprioceptive information to control their movements, both when on the ground and when airborne. Exploiting visual information for motor control is significantly simplified if the eyes remain aligned with the external horizon. In fast flying insects, head rotations relative to the body enable gaze stabilisation during highspeed manoeuvres or externally caused attitude changes due to turbulent air. Previous behavioural studies into gaze stabilisation suffered from the dynamic properties of the supplying sensor systems and those of the neck motor system being convolved. Specifically, stabilisation of the head in Dipteran flies responding to induced thorax roll involves feed forward information from the mechanosensory halteres, as well as feedback information from the visual systems. To fully understand the functional design of the blowfly gaze stabilisation system as a whole, the neck motor system needs to be investigated independently. Through X-ray micro-computed tomography (ÎĽCT), high resolution 3D data has become available, and using staining techniques developed in collaboration with the Natural History Museum London, detailed anatomical data can be extracted. This resulted in a full 3- dimensional anatomical representation of the 21 neck muscle pairs and neighbouring cuticula structures which comprise the blowfly neck motor system. Currently, on the work presented in my PhD thesis, ÎĽCT data are being used to infer function from structure by creating a biomechanical model of the neck motor system. This effort aims to determine the specific function of each muscle individually, and is likely to inform the design of artificial gaze stabilisation systems. Any such design would incorporate both sensory and motor systems as well as the control architecture converting sensor signals into motor commands under the given physical constraints of the system as a whole.Open Acces

    Magnetic Resonance Imaging of the fetal cardiovascular system and congenital heart disease

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    An early diagnosis of congenital heart diseases (CHD) has important prognostic impact. Prenatal echocardiography is an indispensable part of prenatal screening in many countries. However, it might provide poor diagnostic quality in some cases. Complementary diagnostic methods for postnatal life are missing prenatally. This work aims to investigate the use of fetal cardiovascular magnetic resonance imaging (MRI) as an adjunct to fetal echocardiography. This manuscript is divided into the anatomical visualization of CHD and the quantification of the impact of fetal motion on cardiovascular flow-measurements. 101 singleton pregnant women carrying fetus with suspected CHD in fetal echocardiography were prospectively recruited for fetal cardiac MRI. In 85 participants 2D and 3D MRI data could be reconstructed successfully and compared to echocardiographic and postnatal data. Furthermore, 10 pregnant women from the first sub study and 10 adult volunteers were recruited. The impact of simulated fetal motion in the adult volunteers was investigated. The artifacts observed during this study were compared to the artefacts in fetal flow-measurements by a three-point scoring system. MRI reconstructions of vascular structures showed a good agreement with 2D-echocardiography, while 3D-MRI reconstructions were superior to 2D-MRI data regarding their quality and diagnostic accuracy. Additional anatomic structures were identified in 10 cases with MRI and could be confirmed postnatally. Flow-measurements corrupted by simulated fetal motion within the middle third of an acquisition showed significant errors in contrast to measurements under motion corruption during the first and last third of the acquisition. The velocity of motion did not have a major impact. A three-point scoring system could readily identify the amount and impact of fetal motion on the later acquisition. 3D fetal cardiac MRI is a reliable imaging method with potential complementary use to fetal echocardiography. Additionally, valid fetal cardiovascular flow-measurements under the face of fetal motion can be reliably identified at the point of their acquisition, already.Die frühe Diagnose angeborener Herzfehler hat eine prognostische Bedeutung. Eine pränatale Echokardiographie ist in vielen Ländern unverzichtbarer Standard pränataler Screening Untersuchungen. Dennoch müssen oft Abstriche bei der Bildqualität gemacht werden. Während postnatal ergänzende Bildgebungstechniken zur Verfügung stehen, fehlen diese Alternativen pränatal. Die vorliegende Arbeit soll die Möglichkeiten der fetalen kardiovaskulären Magnetresonanztomographie (MRT) als ergänzende Diagnostik zur fetalen Echokardiographie untersuchen. Die vorliegende Arbeit ist untergliedert in die anatomische Darstellung angeborener Herzfehler mittels der MRT und die Untersuchung des Einflusses fetaler Bewegung auf kardiovaskuläre Flussmessungen. 101 schwangere Teilnehmerinnen mit Feten mit dem V.a. einen angeborenen Herzfehler in der fetalen Echokardiographie wurden prospektiv für eine fetale Kardio-MRT rekrutiert. 2D und 3D Bilddatenrekonstruktionen von 85 Feten der Teilnehmerinnen konnten mit den echokardiographischen, sowie postnatalen Daten verglichen werden. Weiterhin wurden 10 erwachsene Proband*innen, sowie 10 schwangere Teilnehmerinnen aus der ersten Substudie rekrutiert. Einflüsse simulierter fetaler Bewegung in den erwachsenen Proband*innen wurden untersucht. Beobachtete Artefakte in den gewonnen Flussmessungen wurden mittels eines Bewertungssystems mit denen der fetalen Messungen verglichen. Vaskuläre Strukturen in MRT-Datensätzen zeigten eine gute Übereinstimmung mit Messungen in echokardiographischen 2D-Datensätzen, wobei 3D-MRT Datensätze hinsichtlich Qualität und diagnostischer Genauigkeit den 2D-MRT Daten überlegen waren. In 10 Fällen gelang die Darstellung zusätzlicher anatomischer Gegebenheiten in der MRT, welche postnatal bestätigt werden konnten. Flussmessungen, welche durch simulierte fetale Bewegung im mittleren Drittel einer Aufnahme verzerrt wurden, wiesen signifikante Fehler auf. Dies konnte bei Messungen unter dem Einfluss fetalen Bewegungen im ersten oder letzten Drittel der Aufnahme nicht beobachtet werden. Die Geschwindigkeit der Bewegungen spielte eine untergeordnete Rolle. Das Ausmaß fetaler Bewegung während einer Aufnahme, sowie ihr Einfluss auf die Flussmessungen kann mittels eines Drei-Punkte-Bewertungssystems zuverlässig identifiziert werden. Fetale Kardio-MRT bietet eine zuverlässige Möglichkeit mittels 3D-Darstellung der fetalen Gefäße die pränatale Echokardiographie als bildgebende Methode zu ergänzen. Zudem können valide Flussmessungen trotz Einfluss fetaler Bewegung zuverlässig zum Zeitpunkt der Aufnahme identifiziert werden

    Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy

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    PURPOSE: Minimally invasive treatments for renal carcinoma offer a low rate of complications and quick recovery. One drawback of the use of computed tomography (CT) for needle guidance is the use of iodinated contrast agents, which require an increased X-ray dose and can potentially cause adverse reactions. The purpose of this work is to generalise the problem of synthetic contrast enhancement to allow the generation of multiple phases on non-contrast CT data from a real-world, clinical dataset without training multiple convolutional neural networks. METHODS: A framework for switching between contrast phases by conditioning the network on the phase information is proposed and compared with separately trained networks. We then examine how the degree of supervision affects the generated contrast by evaluating three established architectures: U-Net (fully supervised), Pix2Pix (adversarial with supervision), and CycleGAN (fully adversarial). RESULTS: We demonstrate that there is no performance loss when testing the proposed method against separately trained networks. Of the training paradigms investigated, the fully adversarial CycleGAN performs the worst, while the fully supervised U-Net generates more realistic voxel intensities and performed better than Pix2Pix in generating contrast images for use in a downstream segmentation task. Lastly, two models are shown to generalise to intra-procedural data not seen during the training process, also enhancing features such as needles and ice balls relevant to interventional radiological procedures. CONCLUSION: The proposed contrast switching framework is a feasible option for generating multiple contrast phases without the overhead of training multiple neural networks, while also being robust towards unseen data and enhancing contrast in features relevant to clinical practice

    Current Challenges in the Application of Algorithms in Multi-institutional Clinical Settings

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    The Coronavirus disease pandemic has highlighted the importance of artificial intelligence in multi-institutional clinical settings. Particularly in situations where the healthcare system is overloaded, and a lot of data is generated, artificial intelligence has great potential to provide automated solutions and to unlock the untapped potential of acquired data. This includes the areas of care, logistics, and diagnosis. For example, automated decision support applications could tremendously help physicians in their daily clinical routine. Especially in radiology and oncology, the exponential growth of imaging data, triggered by a rising number of patients, leads to a permanent overload of the healthcare system, making the use of artificial intelligence inevitable. However, the efficient and advantageous application of artificial intelligence in multi-institutional clinical settings faces several challenges, such as accountability and regulation hurdles, implementation challenges, and fairness considerations. This work focuses on the implementation challenges, which include the following questions: How to ensure well-curated and standardized data, how do algorithms from other domains perform on multi-institutional medical datasets, and how to train more robust and generalizable models? Also, questions of how to interpret results and whether there exist correlations between the performance of the models and the characteristics of the underlying data are part of the work. Therefore, besides presenting a technical solution for manual data annotation and tagging for medical images, a real-world federated learning implementation for image segmentation is introduced. Experiments on a multi-institutional prostate magnetic resonance imaging dataset showcase that models trained by federated learning can achieve similar performance to training on pooled data. Furthermore, Natural Language Processing algorithms with the tasks of semantic textual similarity, text classification, and text summarization are applied to multi-institutional, structured and free-text, oncology reports. The results show that performance gains are achieved by customizing state-of-the-art algorithms to the peculiarities of the medical datasets, such as the occurrence of medications, numbers, or dates. In addition, performance influences are observed depending on the characteristics of the data, such as lexical complexity. The generated results, human baselines, and retrospective human evaluations demonstrate that artificial intelligence algorithms have great potential for use in clinical settings. However, due to the difficulty of processing domain-specific data, there still exists a performance gap between the algorithms and the medical experts. In the future, it is therefore essential to improve the interoperability and standardization of data, as well as to continue working on algorithms to perform well on medical, possibly, domain-shifted data from multiple clinical centers

    A deep learning model for drug screening and evaluation in bladder cancer organoids

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    Three-dimensional cell tissue culture, which produces biological structures termed organoids, has rapidly promoted the progress of biological research, including basic research, drug discovery, and regenerative medicine. However, due to the lack of algorithms and software, analysis of organoid growth is labor intensive and time-consuming. Currently it requires individual measurements using software such as ImageJ, leading to low screening efficiency when used for a high throughput screen. To solve this problem, we developed a bladder cancer organoid culture system, generated microscopic images, and developed a novel automatic image segmentation model, AU2Net (Attention and Cross U2Net). Using a dataset of two hundred images from growing organoids (day1 to day 7) and organoids with or without drug treatment, our model applies deep learning technology for image segmentation. To further improve the accuracy of model prediction, a variety of methods are integrated to improve the model’s specificity, including adding Grouping Cross Merge (GCM) modules at the model’s jump joints to strengthen the model’s feature information. After feature information acquisition, a residual attentional gate (RAG) is added to suppress unnecessary feature propagation and improve the precision of organoids segmentation by establishing rich context-dependent models for local features. Experimental results show that each optimization scheme can significantly improve model performance. The sensitivity, specificity, and F1-Score of the ACU2Net model reached 94.81%, 88.50%, and 91.54% respectively, which exceed those of U-Net, Attention U-Net, and other available network models. Together, this novel ACU2Net model can provide more accurate segmentation results from organoid images and can improve the efficiency of drug screening evaluation using organoids
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