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Image Segmentation And Uncertainty
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Book Synopsis Image Segmentation and Uncertainty by : Roland Wilson
Download or read book Image Segmentation and Uncertainty written by Roland Wilson and published by Wiley-Blackwell. This book was released on 1988-03-09 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the first unified theory of image segmentation, written by the winners of the 1985 Pattern Recognition Society medal. Until now, image processing algorithms have always been beset by uncertainties, no one method proving completely satisfactory. Wilson and Spann tackle the problem of uncertainty head-on. They describe a new class of algorithms (based, in part, on quadtrees) and demonstrate their applications, including grey level and texture segmentation. These algorithms produce excellent results in a wide range of synthetic and natural data. Provides many examples of applications from medicine to remote sensing.
Book Synopsis Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis by : Carole H. Sudre
Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis written by Carole H. Sudre and published by Springer Nature. This book was released on 2020-10-05 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.
Book Synopsis Probabilistic Models for Interactive Medical Image Segmentation and Uncertainty Estimation by : Tzachi Hershkovich
Download or read book Probabilistic Models for Interactive Medical Image Segmentation and Uncertainty Estimation written by Tzachi Hershkovich and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fully-automated segmentation algorithms offer fast, objective, and reproducible results for large data collections. However these techniques cannot handle tasks that require contextual knowledge not readily available in the images alone. Thus, the supervision of an expert is necessary. We present a generative model for image segmentation, based on a bayesian inference. Not only does our approach support an intuitive and convenient user interaction subject to the bottom-up constraints introduced by the image intensities, it also facilitates the main limitations of a human observer - 3D visualization and modality fusion. The user "dialogue" with the segmentation algorithm, via several mouse clicks in regions of disagreement, is formulated as an additional, spatial term in a global cost functional for 3D segmentation. The method is exemplified for the segmentation of regions of interest (ROIs) in three different datasets: cerebral hemorrhages (CH) in human brain CT scans; ventricles in degenerative mice brain MRI and tumors in multi-modal human brain MRI. In significant amount of study cases, user guidance might not be sufficient to solve image segmentation ambiguities. Hence, estimation of the uncertainty margins of the extracted anatomical structure or pathology boundaries should be considered. In this part of my thesis I study the concept of segmentation uncertainty of clinical images, acknowledging its great importance to patients follow up, user-interaction guidance and morphology-based population studies. We propose a novel approach for model-dependent uncertainty estimation for image segmentation. The key-contribution is an alternating, iterative algorithm for the generation of an image-specific uncertainty map. This is accomplished by defining a consistency-based measure and applying it to segmentation samples to estimate the uncertainty margins as well as the midline segmentation. We utilize the stochastic active contour framework as our segmentation generator, yet any sampling method can be applied. The method is validated on synthetic data for well-defined objects blurred with known Gaussian kernels. Qualitative and quantitative proofs of concept are further provided by an application of the proposed consistency-based algorithm to ensembles of stochastic segmentations of brain hemorrhage in CT scans. -- abstract.
Book Synopsis Uncertainty Quantification in Image Segmentation Using the Ambrosio-Tortorelli Approximation of the Mumford-Shah Energy by : Michael Hintermüller
Download or read book Uncertainty Quantification in Image Segmentation Using the Ambrosio-Tortorelli Approximation of the Mumford-Shah Energy written by Michael Hintermüller and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The quantification of uncertainties in image segmentation based on the Mumford-Shah model is studied. The aim is to address the error propagation of noise and other error types in the original image to the restoration result and especially the reconstructed edges (sharp image contrasts). Analytically, we rely on the Ambrosio-Tortorelli approximation and discuss the existence of measurable selections of its solutions as well as sampling-based methods and the limitations of other popular methods. Numerical examples illustrate the theoretical findings.
Book Synopsis Measurement Uncertainty in Cell Image Segmentation Data Analysis by : Jin Chu Wu
Download or read book Measurement Uncertainty in Cell Image Segmentation Data Analysis written by Jin Chu Wu and published by . This book was released on 2013 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures by : Hayit Greenspan
Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures written by Hayit Greenspan and published by Springer Nature. This book was released on 2019-10-10 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
Book Synopsis 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) by : IEEE Staff
Download or read book 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) written by IEEE Staff and published by . This book was released on 2021-04-13 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging ISBI 2021 will be the 18th meeting in this series The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging The 2021 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation
Book Synopsis From Uncertainty to Adaptivity by : Kung-Hao Liang
Download or read book From Uncertainty to Adaptivity written by Kung-Hao Liang and published by . This book was released on 1997 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning in Medical Imaging by : Mingxia Liu
Download or read book Machine Learning in Medical Imaging written by Mingxia Liu and published by Springer Nature. This book was released on 2020-10-02 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.
Book Synopsis Uncertainty for Safe Utilization of Machine Learning in Medical Imaging by : Carole H. Sudre
Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging written by Carole H. Sudre and published by Springer Nature. This book was released on 2023-10-06 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2023, held in conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. For this workshop, 21 papers from 32 submissions were accepted for publication. The accepted papers cover the fields of uncertainty estimation and modeling, as well as out of distribution management, domain shift robustness, Bayesian deep learning and uncertainty calibration.
Book Synopsis Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health by : Shadi Albarqouni
Download or read book Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health written by Shadi Albarqouni and published by Springer Nature. This book was released on 2021-09-23 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the First MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with MICCAI 2021, in September/October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DART 2021 accepted 13 papers from the 21 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. For FAIR 2021, 10 papers from 17 submissions were accepted for publication. They focus on Image-to-Image Translation particularly for low-dose or low-resolution settings; Model Compactness and Compression; Domain Adaptation and Transfer Learning; Active, Continual and Meta-Learning.
Book Synopsis Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis by : Carole H. Sudre
Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis written by Carole H. Sudre and published by Springer Nature. This book was released on 2021-09-30 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.
Book Synopsis Information Processing in Medical Imaging by : Aasa Feragen
Download or read book Information Processing in Medical Imaging written by Aasa Feragen and published by Springer Nature. This book was released on 2021-06-20 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 27th International Conference on Information Processing in Medical Imaging, IPMI 2021, which was held online during June 28-30, 2021. The conference was originally planned to take place in Bornholm, Denmark, but changed to a virtual format due to the COVID-19 pandemic. The 59 full papers presented in this volume were carefully reviewed and selected from 200 submissions. They were organized in topical sections as follows: registration; causal models and interpretability; generative modelling; shape; brain connectivity; representation learning; segmentation; sequential modelling; learning with few or low quality labels; uncertainty quantification and generative modelling; and deep learning.
Book Synopsis Bayesian Reinforcement Learning by : Mohammad Ghavamzadeh
Download or read book Bayesian Reinforcement Learning written by Mohammad Ghavamzadeh and published by . This book was released on 2015-11-18 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.
Book Synopsis Dynamic Data Driven Applications Systems by : Frederica Darema
Download or read book Dynamic Data Driven Applications Systems written by Frederica Darema and published by Springer Nature. This book was released on 2020-11-02 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Conference on Dynamic Data Driven Application Systems, DDDAS 2020, held in Boston, MA, USA, in October 2020. The 21 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 40 submissions. They cover topics such as: digital twins; environment cognizant adaptive-planning systems; energy systems; materials systems; physics-based systems analysis; imaging methods and systems; and learning systems.
Book Synopsis Uncertainty Principles and Gabor Framework in Image Segmentation, Analysis and Synthesis by : Chen Sagiv
Download or read book Uncertainty Principles and Gabor Framework in Image Segmentation, Analysis and Synthesis written by Chen Sagiv and published by . This book was released on 2006 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Automated Segmentation and Uncertainty Quantification for Image-based Cardiovascular Modeling with Convolutional Neural Networks by : Gabriel Dominic Maher
Download or read book Automated Segmentation and Uncertainty Quantification for Image-based Cardiovascular Modeling with Convolutional Neural Networks written by Gabriel Dominic Maher and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we accelerate and extend a path-planning based patient-specific modeling method commonly used for anatomic model creation for cardiovascular fluid dynamics simulations. We further address a longstanding open question of how realistic patient-specific model geometry variability influences simulation output uncertainty. Model building is accelerated by using recently developed deep learning methods and convolutional neural networks to automatically generate vessel surfaces from image data. We enable the quantification of simulation output uncertainty due to geometry variation by modeling the probability distribution of vessel surfaces using convolutional Bayesian dropout networks. In the first part of this thesis we use fully-convolutional neural networks (FCNN) to generate 2D cardiovascular segmentations. Given vessel pathlines, the neural network generates 2D vessel enhancement images along the pathlines. Thereafter, vessel segmentations are extracted using the marching-squares algorithm, which are then used to construct 3D cardiovascular models. The neural network is trained using a novel loss function, tailored for partially labeled segmentation data. An automated quality control method is also developed, allowing promising segmentations to be selected. Thereafter we develop machine learning models to directly predict vessel lumen surface points using a regression formulation. In contrast to the previous method, which identifies the vessel lumen through binary pixel classification, formulating vessel lumen detection as a regression task allows predictions to be made with human expert level accuracy. The regression formulation enables machine learning models to be trained end-to-end for vessel lumen detection without post-processing steps that reduce accuracy. In the third part of this thesis we propose a novel approach to sample from a distribution of patient-specific models for a given image volume. The method uses the previously developed vessel lumen regression networks, combined with dropout layers, to enable Bayesian sampling of vessel geometries. The networks are then applied in the path-planning patient-specific modeling pipeline to generate families of cardiovascular models. A key innovation is the ability to learn geometric uncertainty directly from training data based on medical images. We then quantify geometric uncertainty for clinically relevant anatomies, and provide detailed analysis of its effects on cardiovascular patient-specific fluid dynamics simulation results. The above methods allow for efficient automation of patient-specific model construction from medical images which greatly accelerate the model construction process and reduce laborious user input. These methods are combined with uncertainty quantification methods that enable assessment of how image-based uncertainty propagates to simulation outputs.
