Improving Medical Image Segmentation By Designing Around Clinical Context
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Improving Medical Image Segmentation By Designing Around Clinical Context
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Author : Darvin Yi
language : en
Publisher:
Release Date : 2021
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 2021 with Image segmentation 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±0.02 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±0.2 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±0.01, 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.
Advances In Clinical Radiology 2023 E Book
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Author : Frank H. Miller
language : en
Publisher: Elsevier Health Sciences
Release Date : 2023-08-01
Advances In Clinical Radiology 2023 E Book written by Frank H. Miller and has been published by Elsevier Health Sciences this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-01 with Medical categories.
Advances in Clinical Radiology reviews the year's most important findings and updates within the field in order to provide radiologists with the current clinical information they need to improve patient outcomes. A distinguished editorial board, led by Dr. Frank H. Miller, identifies key areas of major progress and controversy and invites preeminent specialists to contribute original articles devoted to these topics. These insightful overviews in clinical radiology inform and enhance clinical practice by bringing concepts to a clinical level and exploring their everyday impact on patient care. - Contains 20 articles on such topics as artificial intelligence and imaging of the liver; lung cancer screening update; musculoskeletal applications of cone-beam computed tomography; contrast-enhanced ultrasound; advances in imaging for headache and sinus disease; and more. - Provides in-depth, clinical reviews in clinical radiology, providing actionable insights for clinical practice. - Presents the latest information in the field under the leadership of an experienced editorial team. Authors synthesize and distill the latest research and practice guidelines to create these timely topic-based reviews.
Medical Image Computing And Computer Assisted Intervention Miccai 2022
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Author : Linwei Wang
language : en
Publisher: Springer Nature
Release Date : 2022-09-15
Medical Image Computing And Computer Assisted Intervention Miccai 2022 written by Linwei Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-15 with Computers categories.
The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.
Research Anthology On Improving Medical Imaging Techniques For Analysis And Intervention
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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2022-09-09
Research Anthology On Improving Medical Imaging Techniques For Analysis And Intervention written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-09 with Medical categories.
Medical imaging provides medical professionals the unique ability to investigate and diagnose injuries and illnesses without being intrusive. With the surge of technological advancement in recent years, the practice of medical imaging has only been improved through these technologies and procedures. It is essential to examine these innovations in medical imaging to implement and improve the practice around the world. The Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention investigates and presents the recent innovations, procedures, and technologies implemented in medical imaging. Covering topics such as automatic detection, simulation in medical education, and neural networks, this major reference work is an excellent resource for radiologists, medical professionals, hospital administrators, medical educators and students, librarians, researchers, and academicians.
Advances In Artificial Intelligence And Electronic Design Technologies
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Author : Zahereel Ishwar Abdul Khalib
language : en
Publisher: Springer Nature
Release Date : 2025-04-15
Advances In Artificial Intelligence And Electronic Design Technologies written by Zahereel Ishwar Abdul Khalib and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
This book showcases innovative approaches driving advancements in relevant fields such as smart manufacturing, Industry 5.0, and robotics. This edition of the Springer Studies in Computational Intelligence (SCI) Series explores cutting-edge applications of computational intelligence. Designed for engineers, industry professionals, and applied researchers, this book effectively bridges theory and real-world implementation. Through a diverse collection of case studies and practical examples, readers will discover how computational intelligence techniques solve complex challenges across various sectors. The book offers actionable deployment strategies, empowering professionals to apply these concepts in their fields. This book cultivates a holistic approach to innovation and problem-solving by synthesizing diverse perspectives within computational intelligence. This book is an essential resource for practitioners and researchers. It features hands-on implementation insights, comprehensive coverage of emerging trends, and a focus on industry-relevant techniques. It equips readers with the knowledge and tools to harness computational intelligence, tackle real-world challenges, and drive meaningful progress in their respective domains. This book contains 50 papers pertaining to the abovementioned topics, providing a rich and diverse exploration of computational intelligence applications and methodologies.
Data Driven Clinical Decision Making Using Deep Learning In Imaging
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Author : M. F. Mridha
language : en
Publisher: Springer Nature
Release Date : 2024-08-13
Data Driven Clinical Decision Making Using Deep Learning In Imaging written by M. F. Mridha and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-13 with Computers categories.
This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.
Handbook Of Medical Image Processing And Analysis
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Author : Isaac Bankman
language : en
Publisher: Elsevier
Release Date : 2008-12-24
Handbook Of Medical Image Processing And Analysis written by Isaac Bankman and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-24 with Computers categories.
The Handbook of Medical Image Processing and Analysis is a comprehensive compilation of concepts and techniques used for processing and analyzing medical images after they have been generated or digitized. The Handbook is organized into six sections that relate to the main functions: enhancement, segmentation, quantification, registration, visualization, and compression, storage and communication.The second edition is extensively revised and updated throughout, reflecting new technology and research, and includes new chapters on: higher order statistics for tissue segmentation; tumor growth modeling in oncological image analysis; analysis of cell nuclear features in fluorescence microscopy images; imaging and communication in medical and public health informatics; and dynamic mammogram retrieval from web-based image libraries.For those looking to explore advanced concepts and access essential information, this second edition of Handbook of Medical Image Processing and Analysis is an invaluable resource. It remains the most complete single volume reference for biomedical engineers, researchers, professionals and those working in medical imaging and medical image processing.Dr. Isaac N. Bankman is the supervisor of a group that specializes on imaging, laser and sensor systems, modeling, algorithms and testing at the Johns Hopkins University Applied Physics Laboratory. He received his BSc degree in Electrical Engineering from Bogazici University, Turkey, in 1977, the MSc degree in Electronics from University of Wales, Britain, in 1979, and a PhD in Biomedical Engineering from the Israel Institute of Technology, Israel, in 1985. He is a member of SPIE. - Includes contributions from internationally renowned authors from leading institutions - NEW! 35 of 56 chapters have been revised and updated. Additionally, five new chapters have been added on important topics incluling Nonlinear 3D Boundary Detection, Adaptive Algorithms for Cancer Cytological Diagnosis, Dynamic Mammogram Retrieval from Web-Based Image Libraries, Imaging and Communication in Health Informatics and Tumor Growth Modeling in Oncological Image Analysis. - Provides a complete collection of algorithms in computer processing of medical images - Contains over 60 pages of stunning, four-color images
Conf Mla 2024
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Author : Mustafa Istanbullu
language : en
Publisher: European Alliance for Innovation
Release Date : 2025-03-11
Conf Mla 2024 written by Mustafa Istanbullu and has been published by European Alliance for Innovation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-11 with Computers categories.
This book constitutes the thoroughly refereed Proceedings of the 2nd International Conference on Machine Learning and Automation, CONF-MLA 2024, held in Adana, Turkey, in November 2024. The 45 full papers presented were carefully reviewed and selected from 102 submissions. The papers reflect the conference sessions as follows: computing, automation, machine learning, robotics and intelligent systems, artificial intelligence and data science.
Medical Image Segmentation Foundation Models Cvpr 2024 Challenge Segment Anything In Medical Images On Laptop
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Author : Jun Ma
language : en
Publisher: Springer Nature
Release Date : 2025-02-18
Medical Image Segmentation Foundation Models Cvpr 2024 Challenge Segment Anything In Medical Images On Laptop written by Jun Ma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-18 with Computers categories.
This LNCS conference set constitutes the proceedings of the First Medical Image Segmentation Challenge, MedSAM on Laptop 2024, Held in Conjunction with CVPR 2024, in Seattle, WA, USA, held in June 2024. The 16 full papers presented were thoroughly reviewed and selected from the 200 submissions. This challenge aims to prompt the development of universal promotable medical image segmentation foundation models that are deployable on laptops or other edge devices without reliance on GPUs.
Advanced Techniques In Medical Imaging
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Author : Dr. Maram Ashok
language : en
Publisher: Quing Publications
Release Date : 2024-02-12
Advanced Techniques In Medical Imaging written by Dr. Maram Ashok and has been published by Quing Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-12 with Computers categories.
Medical imaging has revolutionised the field of healthcare, providing critical insights and aiding in accurate diagnoses. This book, "Advanced Techniques in Medical Imaging: Computer Vision and Machine Learning Approaches," begins with an introduction to the world of medical imaging, highlighting its importance and evolution. We then delve into the fundamentals of computer vision, a key component in interpreting complex medical images. Following this, an introduction to machine learning sets the stage for understanding how these powerful algorithms can be harnessed to analyse medical data. The book covers a wide range of topics, including image segmentation techniques that allow for precise identification of structures within medical images and feature extraction and representation, which are crucial for converting image data into usable information. We explore medical image classification, illustrating how different algorithms can differentiate between various conditions. A significant portion of the book is dedicated to deep learning architectures, which have shown remarkable success in medical diagnosis. We also discuss computer-aided diagnosis systems, becoming indispensable tools for clinicians. Finally, the book addresses the challenges faced in this field. It looks towards future directions, ensuring that readers are equipped with a comprehensive understanding of the current landscape and the potential advancements in medical imaging technology. This book aims to provide a thorough grounding in the latest techniques and approaches, making it an invaluable resource for researchers, practitioners, and students involved in the intersection of medical imaging, computer vision, and machine learning.