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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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.
Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics
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Author : Le Lu
language : en
Publisher: Springer Nature
Release Date : 2019-09-19
Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics written by Le Lu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-19 with Computers categories.
This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval. The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
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 : 2013
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 2013 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.
Deep Learning And Convolutional Neural Networks For Medical Image Computing
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Author : Le Lu
language : en
Publisher: Springer
Release Date : 2017-07-12
Deep Learning And Convolutional Neural Networks For Medical Image Computing written by Le Lu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-12 with Computers categories.
This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
High Dimensional Convolutional Neural Networks For 3d Perception
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Author : Christopher Bongsoo Choy
language : en
Publisher:
Release Date : 2020
High Dimensional Convolutional Neural Networks For 3d Perception written by Christopher Bongsoo Choy 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.
The automation of mechanical tasks brought the modern world unprecedented prosperity and comfort. However, the majority of automated tasks have been simple mechanical tasks that only require repetitive motion. Tasks that require visual perception and high-level cognition still have become the last frontiers of automation. Many of these tasks require visual perception such as automated warehouses where robots package items in disarray, autonomous driving where autonomous agents localize themselves, identify and track other dynamic objects in the 3D world. This ability to represent, identify, and interpret visual three-dimensional data to understand the underlying three-dimensional structure in the real world is known as 3D perception. In this dissertation, we propose learning-based approaches to tackle challenges in 3D perception. Specifically, we propose a set of high-dimensional convolutional neural networks for three categories of problems in 3D perception: reconstruction, representation learning, and registration. Reconstruction is the first step that generates 3D point clouds or meshes from a set of sensory inputs. We present supervised reconstruction methods using 3D convolutional neural networks that take a set of images as input and generate 3D occupancy patterns in a grid as output. We train the networks with a large-scale 3D shape dataset to generate a set of images rendered from various viewpoints validate the approach on real image datasets. However, supervised reconstruction requires 3D shapes as labels for all images, which are expensive to generate. Instead, we propose using a set of foreground masks and unlabeled real 3D shapes to train the reconstruction network as weaker supervision. Combined with the learned constraint, we train the reconstruction system with as few as 1 image and show that the proposed model without direct 3D supervision. In the second part of the dissertation, we present sparse tensor networks, neural networks for spatially sparse tensors. As we increase the spatial dimension, the sparsity of input data decreases drastically as the volume of the space increases exponentially. Sparse tensor networks exploit such inherent sparsity in the input data and efficiently process them. With the sparse tensor network, we create a 4-dimensional convolutional network for spatio-temporal perception for 3D scans or a sequence of 3D scans (3D video). We show that 4-dimensional convolutional neural networks can effectively make use of temporal consistency and improve the accuracy of segmentation. Next, we use the sparse tensor networks for geometric representation learning to capture both local and global 3D structures accurately for correspondences and registration. We propose fully convolutional networks and new types of metric learning losses that allow neurons to capture large context while capturing local spatial geometry. We experimentally validate our approach on both indoor and outdoor datasets and show that the network outperforms the state-of-the-art method while being a few orders of magnitude faster. In the third and the last part of the dissertation, we discuss high-dimensional pattern recognition problems in image and 3D registration. We first propose high-dimensional convolutional networks from 4 to 32-dimensional spaces and analyze the geometric pattern recognition capacity of these high-dimensional convolutional networks for linear regression problems. Next, we show that the 3D correspondences form a hyper-surface in 6-dimensional space; and 2D correspondences form a 4-dimensional hyper-conic section, which we detect using high-dimensional convolutional networks. We extend the proposed high-dimensional convolutional networks for differentiable 3D registration and propose three core modules for this: a 6-dimensional convolutional neural network for correspondence confidence prediction; a differentiable Weighted Procrustes method for closed-form pose estimation; and a robust gradient-based 3D rigid transformation optimizer for pose refinement. Experiments demonstrate that our approach outperforms state-of-the-art learning-based and classical methods on real-world data while maintaining efficiency.
Improving Deep Neural Network Training With Batch Size And Learning Rate Optimization For Head And Neck Tumor Segmentation On 2d And 3d Medical Images
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Author : Zachariah Douglas
language : en
Publisher:
Release Date : 2022
Improving Deep Neural Network Training With Batch Size And Learning Rate Optimization For Head And Neck Tumor Segmentation On 2d And 3d Medical Images written by Zachariah Douglas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Medical imaging is a key tool used in healthcare to diagnose and prognose patients by aiding the detection of a variety of diseases and conditions. In practice, medical image screening must be performed by clinical practitioners who rely primarily on their expertise and experience for disease diagnosis. The ability of convolutional neural networks (CNNs) to extract hierarchical features and determine classifications directly from raw image data makes CNNs a potentially useful adjunct to the medical image analysis process. A common challenge in successfully implementing CNNs is optimizing hyperparameters for training. In this study, we propose a method which utilizes scheduled hyperparameters and Bayesian optimization to classify cancerous and noncancerous tissues (i.e., segmentation) from head and neck computed tomography (CT) and positron emission tomography (PET) scans. The results of this method are compared using CT imaging with and without PET imaging for 2D and 3D image segmentation models.
Deep Network Design For Medical Image Computing
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Author : Haofu Liao
language : en
Publisher: Academic Press
Release Date : 2022-08-24
Deep Network Design For Medical Image Computing written by Haofu Liao and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-24 with Computers categories.
Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more. This book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems. - Explains design principles of deep learning techniques for MIC - Contains cutting-edge deep learning research on MIC - Covers a broad range of MIC tasks, including the classification, detection, segmentation, registration, reconstruction and synthesis of medical images
Automated Brain Lesion Detection And Segmentation Using Mr Images
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Author : Nabizadeh Nooshin
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2015-07-27
Automated Brain Lesion Detection And Segmentation Using Mr Images written by Nabizadeh Nooshin and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-27 with categories.
Computer vision and machine learning allows the image data to be seen by a computer or machine as a person would see it. This is a complex concept for a computer to comprehend since computers do not understand the three-dimensional perspective as a person views and understands it. Computer vision has variety of applications in industry, medicine, surveillance systems, video analysis, robotic, and etc. Image segmentation is one of the most challenging topics in computer vision and machine learning. As an application of image segmentation in biomedical research is to localize some specific cells and tissues, e.g., tumor or stroke in magnetic resonance images (MRI). Medical image segmentation helps physicians to find these lesions more accurately, and it can be great source of information in emergency cases that specialist is not available. Therefore, it is an important process in computerized medical imaging. Automated segmentation of brain lesions in MRI is a difficult procedure due to the variability and complexity of the location, size, shape, and texture of these lesions. This study presents four algorithms for brain lesion detection and segmentation using MR images.
Unsupervised Two Dimensional And Three Dimensional Image Segmentation
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Author : Philippe Schroeter
language : en
Publisher:
Release Date : 1996
Unsupervised Two Dimensional And Three Dimensional Image Segmentation written by Philippe Schroeter and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with categories.
Improving Medical Image Segmentation By Designing Around Clinical Context
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Author : Darvin Yi
language : en
Publisher:
Release Date : 2020
Improving Medical Image Segmentation By Designing Around Clinical Context written by Darvin Yi 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.
The rise of deep learning (DL) has created many novel algorithms for segmentation, which has in turn revolutionized the field of medical image segmentation. However, several distinctions between the field of natural and medical computer vision necessitates specialized algorithms to optimize performance, including the multi-modality of medical data, the differences in imaging protocols between centers, and the limited amount of annotated data. These differences lead to limitations when applying current state of the art computer vision methods on medical imaging. For segmentation, the major gaps our algorithms must bridge to become clinically useful are: (1) generalize to different imaging protocols, (2) become robust to training on noisy labels, and (3) generally improve segmentation performance. The current rigorous deep learning architectures are not robust to having missing input modalities after training a network, which makes our networks unable to run inference on new data taken with a different imaging protocol. By training our algorithms without taking into account the mutability of imaging protocols, we heavily limit the deployability of our algorithms. Our current training paradigm also needs pristine segmentation labels, which necessitates a large time investment by expert annotators. By training our algorithms with an underlying assumption that there is no noise in our labels with harsh loss functions like cross entropy, we create a need for clean labels. This limits our datasets from being fully largely scalable to the same size as natural computer vision datasets, as disease segmentations on medical images require more time and effort to annotate than natural images with semantic classes. Finally, current state of the art performance on difficult segmentation tasks like brain metastases is just not enough to be clinically useful. We will need to explore new ways of designing and ensembling networks to increase segmentation performance should we aim to deploy these algorithms in any clinically relevant environment. We hypothesize that by changing neural network architectures and loss functions to account for noisy data rather than assuming consistent imaging protocols and pristine labels, we can encode more robustness into our trained networks and improve segmentation performance on medical imaging tasks. In our experiments, we will test several different networks whose architecture and loss functions have been motivated by realistic and clinically relevant situations. For these experiments, we chose the model system of brain metastases lesion detection and segmentation, a difficult problem due to the high count and small size of the lesions. It is also an important problem due to the need to assess the effects of treatment by tracking changes in tumor burden. In this dissertation, we present the following specific aims: (1) optimizing deep learning performance on brain metastases segmentation, (2) training networks to be robust to coarse annotations and missing data, and (3) validating our methodology on three different secondary tasks. Our trained baseline performance (state of the art) performs brain metastases segmentation modestly, giving us mAP values of $0.46\pm0.02$ and DICE scores of 0.72. Changing our architectures to account for different pulse sequence integration methods does not improve our values by much, giving us a model mAP improvement to $0.48\pm0.2$ and no improvement in DICE score. However, through investigating pulse sequence integration, we developed a novel input-level dropout training scheme that holds out certain pulse sequences randomly during different iterations of training our deep net. This trains our network to be robust to missing pulse sequences in the future, at no cost to performance. We then developed two additional robustness training schemes that enable training on data annotations that have a lot of noise. We prove that we are able to lose no performance when degrading 70\% of our segmentation annotations with spherical approximations, and show a loss of 5\% performance when degrading 90\% of our annotations. Similarly, when we censor our 50\% of our annotated lesions (simulating a 50\% False Negative Rate), we can preserve 95\% of the performance by utilizing a novel lopsided bootstrap loss. Using these ideas, we use the lesion-based censoring technique as the base of a novel ensembling method we named Random Bundle. This network increased our mAP value $0.65\pm0.01$, an increase of about 40\%. We validate our methods on three different secondary datasets. By validating our methods work on brain metastases data from Oslo University Hospital, we show that our methods are robust to cross-center data. By validating our methods on the MICCAI BraTS dataset, we show that our methods are robust to magnetic resonance images of a different disorder. Finally, by validating our methods on diabetic retinopathy micro-aneurysms on fundus photographs, we show that our methods are robust across imaging domains and organ systems. Our experiments support our claims that (1) designing architectures with a focus on how pulse sequences interact will encode robustness for different imaging protocols, (2) creating custom loss functions around expected annotation errors will make our networks more robust to those errors, and (3) the overall performance of our networks can be improved by using these novel architectures and loss functions.