Structural Priors For Multiobject Semi Automatic Segmentation Of Three Dimensional Medical Images Via Clustering And Graph Cut Algorithms
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Structural Priors For Multiobject Semi Automatic Segmentation Of Three Dimensional Medical Images Via Clustering And Graph Cut Algorithms
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Author : Razmig Kéchichian
language : en
Publisher:
Release Date : 2020
Structural Priors For Multiobject Semi Automatic Segmentation Of Three Dimensional Medical Images Via Clustering And Graph Cut Algorithms written by Razmig Kéchichian and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
We develop a generic Graph Cut-based semiautomatic multiobject image segmentation method principally for use in routine medical applications ranging from tasks involving few objects in 2D images to fairly complex near whole-body 3D image segmentation. The flexible formulation of the method allows its straightforward adaption to a given application.\linebreak In particular, the graph-based vicinity prior model we propose, defined as shortest-path pairwise constraints on the object adjacency graph, can be easily reformulated to account for the spatial relationships between objects in a given problem instance. The segmentation algorithm can be tailored to the runtime requirements of the application and the online storage capacities of the computing platform by an efficient and controllable Voronoi tessellation clustering of the input image which achieves a good balance between cluster compactness and boundary adherence criteria. Qualitative and quantitative comprehensive evaluation and comparison with the standard Potts model confirm that the vicinity prior model brings significant improvements in the correct segmentation of distinct objects of identical intensity, the accurate placement of object boundaries and the robustness of segmentation with respect to clustering resolution. Comparative evaluation of the clustering method with competing ones confirms its benefits in terms of runtime and quality of produced partitions. Importantly, compared to voxel segmentation, the clustering step improves both overall runtime and memory footprint of the segmentation process up to an order of magnitude virtually without compromising the segmentation quality.
Cloud Based Benchmarking Of Medical Image Analysis
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Author : Allan Hanbury
language : en
Publisher: Springer
Release Date : 2017-05-16
Cloud Based Benchmarking Of Medical Image Analysis written by Allan Hanbury and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-16 with Medical categories.
This book is open access under a CC BY-NC 2.5 license. This book presents the VISCERAL project benchmarks for analysis and retrieval of 3D medical images (CT and MRI) on a large scale, which used an innovative cloud-based evaluation approach where the image data were stored centrally on a cloud infrastructure and participants placed their programs in virtual machines on the cloud. The book presents the points of view of both the organizers of the VISCERAL benchmarks and the participants. The book is divided into five parts. Part I presents the cloud-based benchmarking and Evaluation-as-a-Service paradigm that the VISCERAL benchmarks used. Part II focuses on the datasets of medical images annotated with ground truth created in VISCERAL that continue to be available for research. It also covers the practical aspects of obtaining permission to use medical data and manually annotating 3D medical images efficiently and effectively. The VISCERAL benchmarks are described in Part III, including a presentation and analysis of metrics used in evaluation of medical image analysis and search. Lastly, Parts IV and V present reports by some of the participants in the VISCERAL benchmarks, with Part IV devoted to the anatomy benchmarks and Part V to the retrieval benchmark. This book has two main audiences: the datasets as well as the segmentation and retrieval results are of most interest to medical imaging researchers, while eScience and computational science experts benefit from the insights into using the Evaluation-as-a-Service paradigm for evaluation and benchmarking on huge amounts of data.
Medical Computer Vision Algorithms For Big Data
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Author : Bjoern Menze
language : en
Publisher: Springer
Release Date : 2014-12-09
Medical Computer Vision Algorithms For Big Data written by Bjoern Menze and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-09 with Computers categories.
This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision: Algorithms for Big Data, MCV 2014, held in Cambridge, MA, USA, in September 2019, in conjunction with the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014. The one-day workshop aimed at exploring the use of modern computer vision technology and "big data" algorithms in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies emphasizing questions of harvesting, organizing and learning from large-scale medical imaging data sets and general-purpose automatic understanding of medical images. The 18 full and 1 short papers presented in this volume were carefully reviewed and selected from 30 submission.
Deformable Meshes For Medical Image Segmentation
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Author : Dagmar Kainmueller
language : en
Publisher: Springer
Release Date : 2014-08-18
Deformable Meshes For Medical Image Segmentation written by Dagmar Kainmueller and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-18 with Computers categories.
Segmentation of anatomical structures in medical image data is an essential task in clinical practice. Dagmar Kainmueller introduces methods for accurate fully automatic segmentation of anatomical structures in 3D medical image data. The author’s core methodological contribution is a novel deformation model that overcomes limitations of state-of-the-art Deformable Surface approaches, hence allowing for accurate segmentation of tip- and ridge-shaped features of anatomical structures. As for practical contributions, she proposes application-specific segmentation pipelines for a range of anatomical structures, together with thorough evaluations of segmentation accuracy on clinical image data. As compared to related work, these fully automatic pipelines allow for highly accurate segmentation of benchmark image data.
Multi Modality State Of The Art Medical Image Segmentation And Registration Methodologies
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Author : Ayman S. El-Baz
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-04-11
Multi Modality State Of The Art Medical Image Segmentation And Registration Methodologies written by Ayman S. El-Baz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-11 with Medical categories.
With the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures present in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning system designs. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration.
Medical Image Segmentation In Volumetric Ct And Mr Images
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Author : Sean Daniel Murphy
language : en
Publisher:
Release Date : 2012
Medical Image Segmentation In Volumetric Ct And Mr Images written by Sean Daniel Murphy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Diagnostic imaging categories.
This portfolio thesis addresses several topics in the field of 3D medical image analysis. Automated methods are used to identify structures and points of interest within the body to aid the radiologist. The automated algorithms presented here incorporate many classical machine learning and imaging techniques, such as image registration, image filtering, supervised classification, unsupervised clustering, morphology and probabilistic modelling. All algorithms are validated against manually collected ground truth. Chapter two presents a novel algorithm for automatically detecting named anatomical landmarks within a CT scan, using a linear registration based atlas framework. The novel scans may contain a wide variety of anatomical regions from throughout the body. Registration is typically posed as a numerical optimisation problem. For this problem the associated search space is shown to be non-convex and so standard registration approaches fail. Specialised numerical optimisation schemes are developed to solve this problem with an emphasis placed on simplicity. A semi-automated algorithm for finding the centrelines of coronary arterial trees in CT angiography scans given a seed point is presented in chapter three. This is a modified classical region growing algorithm whereby the topology and geometry of the tree are discovered as the region grows. The challenges presented by the presence of large organs and other extraneous material in the vicinity of the coronary trees is mitigated by the use of an efficient modified 3D top-hat transform. Chapter four compares the accuracy of three unsupervised clustering algorithms when applied to automated tissue classification within the brain on 3D multi-spectral MR images. Chapter five presents a generalised supervised probabilistic framework for the segmentation of structures/tissues in medical images called a spatially varying classifier (SVC). This algorithm leverages off non-rigid registration techniques and is shown to be a generalisation of atlas based techniques and supervised intensity based classification. This is achieved by constructing a multivariate Gaussian classifier for each voxel in a reference scan. The SVC is applied in the context of tissue classification in multi-spectral MR images in chapter six, by simultaneously extracting the brain and classifying the tissues types within it. A specially designed pre-processing pipeline is presented which involves inter-sequence registration, spatial normalisation and intensity normalisation. The SVC is then applied to the problem of multi-compartment heart segmentation in CT angiography data with minimal modification. The accuracy of this method is shown to be comparable to other state of the art methods in the field.
Training Of A Three Dimensional Graph Bipartite Densenet Using Adversarial Learning And Weak Supervision For Lesion Detection And Segmentation In Three Dimensional Medical Images
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Author :
language : en
Publisher:
Release Date : 2019
Training Of A Three Dimensional Graph Bipartite Densenet Using Adversarial Learning And Weak Supervision For Lesion Detection And Segmentation In Three Dimensional Medical Images written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Segmentation Classification And Registration Of Multi Modality Medical Imaging Data
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Author : Nadya Shusharina
language : en
Publisher: Springer Nature
Release Date : 2021-03-12
Segmentation Classification And Registration Of Multi Modality Medical Imaging Data written by Nadya Shusharina and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-12 with Computers categories.
This book constitutes three challenges that were held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020*: the Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images Challenge, the Learn2Reg Challenge, and the Thyroid Nodule Segmentation and Classification in Ultrasound Images Challenge. The 19 papers presented in this volume were carefully reviewed and selected form numerous submissions. The ABCs challenge aims to identify the best methods of segmenting brain structures that serve as barriers to the spread of brain cancers and structures to be spared from irradiation, for use in computer assisted target definition for glioma and radiotherapy plan optimization. The papers of the L2R challenge cover a wide spectrum of conventional and learning-based registration methods and often describe novel contributions. The main goal of the TN-SCUI challenge is to find automatic algorithms to accurately segment and classify the thyroid nodules in ultrasound images. *The challenges took place virtually due to the COVID-19 pandemic.
Experiments In Medical Image Segmentations
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Author : Yufeng Cao
language : en
Publisher:
Release Date : 2019
Experiments In Medical Image Segmentations written by Yufeng Cao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Non-invasive Radiology Imaging (e.g. CT, MRI, and PET) have been utilized tremendously in medical study for disease diagnosis, prognostication, and monitoring therapeutic response. And segmenting medical image for regions of interest is an essential step in computer assisted clinical interventions. Tumor detection in biomedical imaging is a time-consuming process for medical professionals and with non-neglectable human variation in recent decades, researchers have developed algorithmic techniques for image processing using a wide variety of mathematical methods, such as statistical modeling, variational techniques, and machine learning. Graph theory is the framework for the study explained in this thesis. We focus on both theoretical and practical aspects, with an emphasis on the experimental, practical parts. On the theoretical part, we propose a graph cut based semi-automatic method for liver segmentation of 2D CT scans into three labels denoting healthy, vessel, or tumor tissue.In chapter one, some definitions and algorithms for networks are introduced. Medical data and the idea of convolution are also introduced. In chapter two, we propose a new model to segment using the image sequences from dynamic CT scan. Also a particular image can be picked from the images sequence and then vectorized using a sequence of convolutions or neighborhoods. We also introduce a method of minimization that we call the Îł method. In chapter three, a moving mean method is developed and tested, using a more traditional train-test partition of the data. In chapter four, we explore the potential of deep neural networks. We decided to begin with U-Net, which consists of a contracting path to capture context and a symmetric expanding path that enables precise localization and we developed for medical data.
From Fully Supervised Single Task To Semi Supervised Multi Task Deep Learning Architectures For Segmentation In Medical Imaging Applications
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Author : S. M. Kamrul Hasan
language : en
Publisher:
Release Date : 2023
From Fully Supervised Single Task To Semi Supervised Multi Task Deep Learning Architectures For Segmentation In Medical Imaging Applications written by S. M. Kamrul Hasan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Deep learning (Machine learning) categories.
"Medical imaging is routinely performed in clinics worldwide for the diagnosis and treatment of numerous medical conditions in children and adults. With the advent of these medical imaging modalities, radiologists can visualize both the structure of the body as well as the tissues within the body. However, analyzing these high-dimensional (2D/3D/4D) images demands a significant amount of time and effort from radiologists. Hence, there is an ever-growing need for medical image computing tools to extract relevant information from the image data to help radiologists perform efficiently. Image analysis based on machine learning has pivotal potential to improve the entire medical imaging pipeline, providing support for clinical decision-making and computer-aided diagnosis. To be effective in addressing challenging image analysis tasks such as classification, detection, registration, and segmentation, specifically for medical imaging applications, deep learning approaches have shown significant improvement in performance. While deep learning has shown its potential in a variety of medical image analysis problems including segmentation, motion estimation, etc., generalizability is still an unsolved problem and many of these successes are achieved at the cost of a large pool of datasets. For most practical applications, getting access to a copious dataset can be very difficult, often impossible. Annotation is tedious and time-consuming. This cost is further amplified when annotation must be done by a clinical expert in medical imaging applications. Additionally, the applications of deep learning in the real-world clinical setting are still limited due to the lack of reliability caused by the limited prediction capabilities of some deep learning models. Moreover, while using a CNN in an automated image analysis pipeline, it’s critical to understand which segmentation results are problematic and require further manual examination. To this extent, the estimation of uncertainty calibration in a semi-supervised setting for medical image segmentation is still rarely reported. This thesis focuses on developing and evaluating optimized machine learning models for a variety of medical imaging applications, ranging from fully-supervised, single-task learning to semi-supervised, multi-task learning that makes efficient use of annotated training data. The contributions of this dissertation are as follows: (1) developing a fully-supervised, single-task transfer learning for the surgical instrument segmentation from laparoscopic images; and (2) utilizing supervised, single-task, transfer learning for segmenting and digitally removing the surgical instruments from endoscopic/laparoscopic videos to allow the visualization of the anatomy being obscured by the tool. The tool removal algorithms use a tool segmentation mask and either instrument-free reference frames or previous instrument-containing frames to fill in (inpaint) the instrument segmentation mask; (3) developing fully-supervised, single-task learning via efficient weight pruning and learned group convolution for accurate left ventricle (LV), right ventricle (RV) blood pool and myocardium localization and segmentation from 4D cine cardiac MR images; (4) demonstrating the use of our fully-supervised memory-efficient model to generate dynamic patient-specific right ventricle (RV) models from cine cardiac MRI dataset via an unsupervised learning-based deformable registration field; and (5) integrating a Monte Carlo dropout into our fully-supervised memory-efficient model with inherent uncertainty estimation, with the overall goal to estimate the uncertainty associated with the obtained segmentation and error, as a means to flag regions that feature less than optimal segmentation results; (6) developing semi-supervised, single-task learning via self-training (through meta pseudo-labeling) in concert with a Teacher network that instructs the Student network by generating pseudo-labels given unlabeled input data; (7) proposing largely-unsupervised, multi-task learning to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two of the foremost critical tasks in medical imaging — segmentation of cardiac structures and reconstruction of the cine cardiac MR images; (8) demonstrating the use of 3D semi-supervised, multi-task learning for jointly learning multiple tasks in a single backbone module – uncertainty estimation, geometric shape generation, and cardiac anatomical structure segmentation of the left atrial cavity from 3D Gadolinium-enhanced magnetic resonance (GE-MR) images. This dissertation summarizes the impact of the contributions of our work in terms of demonstrating the adaptation and use of deep learning architectures featuring different levels of supervision to build a variety of image segmentation tools and techniques that can be used across a wide spectrum of medical image computing applications centered on facilitating and promoting the wide-spread computer-integrated diagnosis and therapy data science."--Abstract.