Recent research on formal verification for Collective Adaptive Systems (CAS) pushed advancements in spatial and spatio-temporal model checking, and as a side result provided novel image analysis methodologies, rooted in logical methods for topological spaces. Medical Imaging (MI) is a field where such technologies show potential for groundbreaking innovation. In this position paper, we present a preliminary investigation centred on applications of spatial model checking to MI. The focus is shifted from pure logics to a mixture of logical, statistical and algorithmic approaches, driven by the logical nature intrinsic to the specification of the properties of interest in the field. As a result, novel operators are introduced, that could as well be brought back to the setting of CAS.
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Spatial aspects of computation are becoming increasingly relevant in Computer Science, especially in the field of collective adaptive systems and when dealing with systems distributed in physical space. Traditional formal verification techniques are well suited to analyse the temporal evolution of programs; however, properties of space are typically not taken into account explicitly. We present a topology-based approach to formal verification of spatial properties depending upon physical space. We define an appropriate logic, stemming from the tradition of topological interpretations of modal logics, dating back to earlier logicians such as Tarski, where modalities describe neighbourhood. We lift the topological definitions to the more general setting of closure spaces, also encompassing discrete, graph-based structures. We extend the framework with a spatial surrounded operator, a propagation operator and with some collective operators. The latter are interpreted over arbitrary sets of points instead of individual points in space. We define efficient model checking procedures, both for the individual and the collective spatial fragments of the logic and provide a proof-of-concept tool.
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Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.
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Neuroimaging in the context of stroke is becoming more and more important. Quantifying and characterizing stroke lesions is still an open challenge. In this paper, we propose a novel framework to solve this problem. The features we use are intensities of patches from multiscale multimodal magnetic resonance (MR) images. We have built random forest classifiers for different parts of the whole brain. A leave-one-out cross-validation result on SISS training data yields 0.55 in Dice score. Abstract. We present our 11-layers deep, double-pathway, 3D Convo-lutional Neural Network, developed for the segmentation of brain lesions. The developed system segments pathology voxel-wise after processing a corresponding multi-modal 3D patch at multiple scales. We demonstrate that it is possible to train such a deep and wide 3D CNN on a small dataset of 28 cases. Our network yields promising results on the task of segmenting ischemic stroke lesions, accomplishing a mean Dice of 64% (66% after postprocessing) on the ISLES 2015 training dataset, ranking among the top entries. Regardless its size, our network is capable of processing a 3D brain volume in 3 minutes, making it applicable to the automated analysis of larger study cohorts. Abstract. Stroke is a common cause of sudden death and disability worldwide. In clinical practice, brain magnetic resonance (MR) scans are used to assess the stroke lesion presence. In this work, we have built a fully automatic stroke lesion segmentation system using 3D brain magnetic resonance (MR) data. The system contains a 3D registration framework and a 3D multi-random forest model trained from the data provided by the Ischemic Stroke Lesion Segmentation (ISLES) challenge of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention. The preliminary test results show that the presented system is capable to detect stroke lesion from 3D brain MRI data. Abstract. This paper presents an automated segmentation framework for ischemic stroke lesion segmentation in multi-spectral MRI images. The framework is based on a random forests (RF), which is an ensemble learning technique that generates several classifiers and combines their results in order to make decisions. In RF, we employ several meaningful features such as intensities, entropy, gradient etc. to classify the voxels in multi-spectral MRI images. The segmentation framework is validated on MICCAI 2015 ISLES challenge training data sets. The performance of the framework is evaluated relative to the manual segmentation (ground truth). The experimental results demonstrate the robustness of the seg-mentation framework, and that it achieves reasonable segmentation accuracy for segmenting the sub-acute ischemic stroke lesion in multi-spectral MRI images. Abstract. Deep Neural Networks (DNNs) are often successful at solving problems for which useful high-level features are not obvious to design. This document presents how DNNs can be used for autom
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Neuroimaging in the context of stroke is becoming more and more important. Quantifying and characterizing stroke lesions is still an open challenge. In this paper, we propose a novel framework to solve this problem. The features we use are intensities of patches from multiscale multimodal magnetic resonance (MR) images. We have built random forest classifiers for different parts of the whole brain. A leave-one-out cross-validation result on SISS training data yields 0.55 in Dice score. Abstract. We present our 11-layers deep, double-pathway, 3D Convo-lutional Neural Network, developed for the segmentation of brain lesions. The developed system segments pathology voxel-wise after processing a corresponding multi-modal 3D patch at multiple scales. We demonstrate that it is possible to train such a deep and wide 3D CNN on a small dataset of 28 cases. Our network yields promising results on the task of segmenting ischemic stroke lesions, accomplishing a mean Dice of 64% (66% after postprocessing) on the ISLES 2015 training dataset, ranking among the top entries. Regardless its size, our network is capable of processing a 3D brain volume in 3 minutes, making it applicable to the automated analysis of larger study cohorts. Abstract. Stroke is a common cause of sudden death and disability worldwide. In clinical practice, brain magnetic resonance (MR) scans are used to assess the stroke lesion presence. In this work, we have built a fully automatic stroke lesion segmentation system using 3D brain magnetic resonance (MR) data. The system contains a 3D registration framework and a 3D multi-random forest model trained from the data provided by the Ischemic Stroke Lesion Segmentation (ISLES) challenge of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention. The preliminary test results show that the presented system is capable to detect stroke lesion from 3D brain MRI data. Abstract. This paper presents an automated segmentation framework for ischemic stroke lesion segmentation in multi-spectral MRI images. The framework is based on a random forests (RF), which is an ensemble learning technique that generates several classifiers and combines their results in order to make decisions. In RF, we employ several meaningful features such as intensities, entropy, gradient etc. to classify the voxels in multi-spectral MRI images. The segmentation framework is validated on MICCAI 2015 ISLES challenge training data sets. The performance of the framework is evaluated relative to the manual segmentation (ground truth). The experimental results demonstrate the robustness of the seg-mentation framework, and that it achieves reasonable segmentation accuracy for segmenting the sub-acute ischemic stroke lesion in multi-spectral MRI images. Abstract. Deep Neural Networks (DNNs) are often successful at solving problems for which useful high-level features are not obvious to design. This document presents how DNNs can be used for autom
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In this work we present a spatial extension of the global model checking algorithm of the temporal logic CTL. This classical verification framework is augmented with ideas coming from the tradition of topological spatial logics. More precisely, we add to CTL the operators of the Spatial Logic of Closure Spaces, including the surrounded operator, with its intended meaning of a point being surrounded by entities satisfying a specific property. The interplay of space and time permits one to define complex spatio-temporal properties. The model checking algorithm that we propose features no particular efficiency optimisations, as it is meant to be a reference specification of a family of more efficient algorithms that are planned for future work. Its complexity depends on the product of temporal states and points of the space. Nevertheless, a prototype model checker has been implemented, made available, and used for experimentation of the application of spatio-temporal verification in the field of collective adaptive systems.
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Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degener-ation, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exu-date-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships .
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Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.
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Several applications such as vessel segmentation, road detection or human airway extraction benefit from curvilinear object segmentation. Although they face similar situations, researchers usually focus on few applications, disregarding the results they could potentially achieve in others. Thus, the same mathematical tools are rediscovered and fine-grained on many occasions. To tackle this problem, we review the state of the art of curvilinear object segmentation in the applications where it has been proved useful. First, we infer the common denominator of what a curvilinear object is and the additional features that each individual application presents. Foremost, we classify the most relevant algorithms to give a complete, cross-application view of how curvilinear objects can be segmented. Further, we present and compare the results they provide when doing so is meaningful. This survey is aimed at understanding under which conditions and which applications some methodologies should be favoured over the others.
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This article reviews ultrasound segmentation methods , in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting 10 papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem.
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Digital topology and geometry refers to the use of topologic and geometric properties and features for images defined in digital grids. Such methods have been widely used in many medical imaging applications, including image segmen-tation, visualization, manipulation, interpolation, registration, surface-tracking, object representation, correction, quantitative morphometry etc. Digital topology and geometry play important roles in medical imaging research by enriching the scope of target outcomes and by adding strong theoretical foundations with enhanced stability, fidelity, and efficiency. This paper presents a comprehensive yet compact survey on results, principles, and insights of methods related to digital topology and geometry with strong emphasis on understanding their roles in various medical imaging applications. Specifically, this paper reviews methods related to distance analysis and path propagation, connectivity, surface-tracking, image segmentation, boundary and centerline detection, topology preservation and local topological properties , skeletonization, and object representation, correction, and quantitative morphometry. A common thread among the topics reviewed in this paper is that their theory and algorithms use the principle of digital path connectivity, path propagation, and neighborhood analysis.
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In this paper we explore the combination of novel spatio-temporal model-checking techniques, and of a recently developed model-based approach to the study of bike sharing systems, in order to detect, visualize and investigate potential problems with bike sharing system configurations. In particular the formation and dynamics of clusters of full stations is explored. Such clusters are likely to be related to the difficulties of users to find suitable parking places for their hired bikes and show up as surprisingly long cycling trips in the trip duration statistics of real bike sharing systems of both small and large cities. Spatio-temporal analysis of the pattern formation may help to explain the phenomenon and possibly lead to alternative bike repositioning strategies aiming at the reduction of the size of such clusters and improving the quality of service.
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Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating infor
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在这项工作中,我们报告了结合IEEE国际生物医学成像研讨会(ISBI)2016和国际医学影像计算机辅助干预会议(MICCAI)2017年组织的肝肿瘤分割基准(LITS)的设置和结果。将24种有效的最先进的肝脏和肝脏肿瘤分段算法应用于一组131个计算机断层扫描(CT)体积,具有不同类型的肿瘤对比度水平(高强度/低强度),组织异常(转移瘤)大小和不同程度的病变。已提交的算法已在70个未公开的卷上进行了测试。该数据集是与七家医院和研究机构合作创建的,由三位独立的放射科医师手动审查。我们发现没有一种算法对肝脏和肿瘤表现最佳。最佳肝脏分割算法的Dice评分为0.96(MICCAI),而对于肿瘤分割,最佳算法评估为0.67(ISBI)和0.70(MICCAI)。 LITS图像数据和手动注释继续通过在线评估系统公开提供,作为持续的基准测试资源。
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Magnetic resonance (MR) imaging is often used to characterize and quantify multiple sclerosis (MS) lesions in the brain and spinal cord. The number and volume of lesions have been used to evaluate MS disease burden, to track the progression of the disease and to evaluate the effect of new pharmaceuticals in clinical trials. Accurate identification of MS lesions in MR images is extremely difficult due to variability in lesion location, size and shape in addition to anatomical variability between subjects. Since manual segmentation requires expert knowledge, is time consuming and is subject to intra-and inter-expert variability, many methods have been proposed to automatically segment lesions. The objective of this study was to carry out a systematic review of the literature to evaluate the state of the art in automated multiple sclerosis lesion segmentation. From 1240 hits found initially with PubMed and Google scholar, our selection criteria identified 80 papers that described an automatic lesion segmentation procedure applied to MS. Only 47 of these included quantitative validation with at least one realistic image. In this paper, we describe the complexity of lesion segmentation, classify the automatic MS lesion segmentation methods found, and review the validation methods applied in each of the papers reviewed. Although many segmentation solutions have been proposed, including some with promising results using MRI data obtained on small groups of patients, no single method is widely employed due to performance issues related to the high variability of MS lesion appearance and differences in image acquisition. The challenge remains to provide segmentation techniques that work in all cases regardless of the type of MS, duration of the disease, or MRI protocol, and this within a comprehensive, standardized validation framework. MS lesion segmentation remains an open problem.
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We address the specification and verification of spatio-temporal behaviours of complex systems, introducing Signal Spatio-Temporal Logic (SSTL). This modal logic extends the Signal Temporal Logic with spatial operators capable of specifying topological properties in a discrete space. The latter is modelled as a weighted graph, and provided with a boolean and a quantitative semantics. Furthermore, we define efficient monitoring algorithms for both the boolean and the quantitative semantics. These are implemented in a Java tool available online. We illustrate the expressiveness of SSTL and the effectiveness of the monitoring procedures on the formation of patterns in a Turing reaction-diffusion system.
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图像生物标记物标准化倡议(IBSI)是一种独立的国际合作,其致力于标准化从获得的成像中提取图像生物标记物,以用于高通量定量图像分析(放射组学)。缺乏可重复性和高通量定量图像分析研究的验证被认为是该领域的主要挑战。这一挑战的部分原因在于基于共识的指导方针和将获得的成像转化为高通量图像生物标记物的过程的定义很少。因此,IBSI致力于提供图像生物标记物命名和定义,基准数据集和基准值,以验证图像处理和图像生物标记,以及报告指南,用于高通量图像分析。
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Research on multimedia information retrieval (MIR) has recently witnessed a booming interest. A prominent feature of this research trend is its simultaneous but independent materialization within several fields of computer science. The resulting richness of paradigms, methods and systems may, on the long run, result in a fragmentation of efforts and slow down progress. The primary goal of this study is to promote an integration of methods and techniques for MIR by contributing a conceptual model that encompasses in a unified and coherent perspective the many efforts that are being produced under the label of MIR. The model offers a retrieval capability that spans two media, text and images, but also several dimensions: form, content and structure. In this way, it reconciles similarity-based methods with semantics-based ones, providing the guidelines for the design of systems that are able to provide a generalized multimedia retrieval service, in which the existing forms of retrieval not only coexist, but can be combined in any desired manner. The model is formulated in terms of a fuzzy description logic, which plays a twofold role: (1) it directly models semantics-based retrieval, and (2) it offers an ideal framework for the integration of the multimedia and multidimensional aspects of retrieval mentioned above. The model also accounts for relevance feedback in both text and image retrieval, integrating known techniques for taking into account user judgments. The implementation of the model is addressed by presenting a decomposition technique that reduces query evaluation to the processing of simpler requests, each of which can be solved by means of widely known methods for text and image retrieval, and semantic processing. A prototype for multidimensional image retrieval is presented that shows this decomposition technique at work in a significant case.
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