Download or read book Landmarking and Segmentation of 3D CT Images written by Shantanu Banik and published by Springer Nature. This book was released on 2022-06-01 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Segmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis (CAD), treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors are difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and size. However, due to the heterogeneous tissue composition of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, segmentation of the tumor mass is a challenging problem. In this context, methods are described in this book for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images. Methods to identify and segment automatically the peripheral artifacts and tissues, the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic surface are described. Techniques are also presented to evaluate quantitatively the results of segmentation of the vertebral column, the spinal canal, the diaphragm, and the pelvic girdle by comparing with the results of independent manual segmentation performed by a radiologist. The use of the landmarks and removal of several tissues and organs are shown to assist in limiting the scope of the tumor segmentation process to the abdomen, to lead to the reduction of the false-positive error, and to improve the result of segmentation of neuroblastic tumors. Table of Contents: Introduction to Medical Image Analysis / Image Segmentation / Experimental Design and Database / Ribs, Vertebral Column, and Spinal Canal / Delineation of the Diaphragm / Delineation of the Pelvic Girdle / Application of Landmarking / Concluding Remarks
Download or read book Landmarking and Segmentation of 3D CT Images written by Shantanu Banik and published by Morgan & Claypool Publishers. This book was released on 2009 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: Segmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis (CAD), treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors are difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and size. However, due to the heterogeneous tissue composition of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, segmentation of the tumor mass is a challenging problem. In this context, methods are described in this book for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images. Methods to identify and segment automatically the peripheral artifacts and tissues, the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic surface are described. Techniques are also presented to evaluate quantitatively the results of segmentation of the vertebral column, the spinal canal, the diaphragm, and the pelvic girdle by comparing with the results of independent manual segmentation performed by a radiologist. The use of the landmarks and removal of several tissues and organs are shown to assist in limiting the scope of the tumor segmentation process to the abdomen, to lead to the reduction of the false-positive error, and to improve the result of segmentation of neuroblastic tumors. Table of Contents: Introduction to Medical Image Analysis / Image Segmentation / Experimental Design and Database / Ribs, Vertebral Column, and Spinal Canal / Delineation of the Diaphragm / Delineation of the Pelvic Girdle / Application of Landmarking / Concluding Remarks
Download or read book 3D Parametric Intensity Models for the Localization of 3D Anatomical Point Landmarks and 3D Segmentation of Human Vessels written by Stefan Wörz and published by IOS Press. This book was released on 2006 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Functional Imaging and Modeling of the Heart written by Olivier Bernard and published by Springer Nature. This book was released on 2023-06-15 with total page 735 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Conference on Functional Imaging and Modeling of the Heart, held in Lyon, France, in June 2023. The 72 full papers were carefully reviewed and selected from 80 submissions. The focus of the papers is on following topics: increased imaging resolutions, data explosion, sophistication of computational models and advent of AI frameworks, while new imaging modalities have emerged (e.g. combined PET-MRI, Spectral CT).
Download or read book Medical Image Recognition Segmentation and Parsing written by S. Kevin Zhou and published by Academic Press. This book was released on 2015-12-11 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects Methods and theories for medical image recognition, segmentation and parsing of multiple objects Efficient and effective machine learning solutions based on big datasets Selected applications of medical image parsing using proven algorithms Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets Includes algorithms for recognizing and parsing of known anatomies for practical applications
Download or read book Machine Learning in Dentistry written by Ching-Chang Ko and published by Springer Nature. This book was released on 2021-07-24 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews all aspects of the use of machine learning in contemporary dentistry, clearly explaining its significance for dental imaging, oral diagnosis and treatment, dental designs, and dental research. Machine learning is an emerging field of artificial intelligence research and practice in which computer agents are employed to improve perception, cognition, and action based on their ability to “learn”, for example through use of big data techniques. Its application within dentistry is designed to promote personalized and precision patient care, with enhancement of diagnosis and treatment planning. In this book, readers will find up-to-date information on different machine learning tools and their applicability in various dental specialties. The selected examples amply illustrate the opportunities to employ a machine learning approach within dentistry while also serving to highlight the associated challenges. Machine Learning in Dentistry will be of value for all dental practitioners and researchers who wish to learn more about the potential benefits of using machine learning techniques in their work.
Download or read book Computational Vision and Medical Image Processing written by João Manuel R.S. Tavares and published by CRC Press. This book was released on 2009-10-01 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Vision and Medical Image Processing, VIPIMAGE 2009 contains the full papers presented at VIPIMAGE 2009 - Second ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, held in Porto, Portugal, on 14-16 October 2009. International contributions from twenty countries provide a comprehensive coverage of the curr
Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2021 written by Marleen de Bruijne and published by Springer Nature. This book was released on 2021-09-22 with total page 711 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.
Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2022 written by Linwei Wang and published by Springer Nature. This book was released on 2022-09-15 with total page 774 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Information Processing in Medical Imaging written by Gábor Székely and published by Springer Science & Business Media. This book was released on 2011-06-29 with total page 806 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 22nd International Conference on Information Processing in Medical Imaging, IPMI 2011, held at Kloster Irsee, Germany, in July 2011. The 24 full papers and 39 poster papers included in this volume were carefully reviewed and selected from 224 submissions. The papers are organized in topical sections on segmentation, statistical methods, shape analysis, registration, diffusion imaging, disease progression modeling, and computer aided diagnosis. The poster sessions deal with segmentation, shape analysis, statistical methods, image reconstruction, microscopic image analysis, computer aided diagnosis, diffusion imaging, functional brain analysis, registration and other related topics.
Download or read book Landmark Based Image Analysis written by Karl Rohr and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Landmarks are preferred image features for a variety of computer vision tasks such as image mensuration, registration, camera calibration, motion analysis, 3D scene reconstruction, and object recognition. Main advantages of using landmarks are robustness w. r. t. lightning conditions and other radiometric vari ations as well as the ability to cope with large displacements in registration or motion analysis tasks. Also, landmark-based approaches are in general com putationally efficient, particularly when using point landmarks. Note, that the term landmark comprises both artificial and natural landmarks. Examples are comers or other characteristic points in video images, ground control points in aerial images, anatomical landmarks in medical images, prominent facial points used for biometric verification, markers at human joints used for motion capture in virtual reality applications, or in- and outdoor landmarks used for autonomous navigation of robots. This book covers the extraction oflandmarks from images as well as the use of these features for elastic image registration. Our emphasis is onmodel-based approaches, i. e. on the use of explicitly represented knowledge in image analy sis. We principally distinguish between geometric models describing the shape of objects (typically their contours) and intensity models, which directly repre sent the image intensities, i. e. ,the appearance of objects. Based on these classes of models we develop algorithms and methods for analyzing multimodality im ages such as traditional 20 video images or 3D medical tomographic images.
Download or read book 3D Parametric Intensity Models for the Localization of 3D Anatomical Point Landmarks and 3D Segmentation of Human Vessels written by Stefan Wörz and published by IOS Press. This book was released on 2006 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Addresses two problems in the field of 3D medical image analysis: the localization of 3D anatomical point landmarks and the segmentation and quantification of 3D tubular structures. This book introduces a different approach for the localization of 3D anatomical point landmarks based on 3D parametric intensity models that are fitted to 3D images.
Download or read book Machine Learning in Medical Imaging written by Chunfeng Lian and published by Springer Nature. This book was released on 2021-09-25 with total page 723 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 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. *The workshop was held virtually.
Download or read book 3D Imaging in Medicine Second Edition written by Jayaram K. Udupa and published by CRC Press. This book was released on 2023-08-18 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a quick and systematic presentation of the principles of biomedical visualization and three-dimensional (3D) imaging. Topics discussed include basic principles and algorithms, surgical planning, neurosurgery, orthopedics, prosthesis design, brain imaging, cardio-pulmonary structure analysis and the assessment of clinical efficacy. Students, scientists, researchers, and radiologists will find 3D Imaging in Medicine a valuable source of information for a variety of actual and potential clinical applications for 3-D imaging.
Download or read book Medical Image Segmentation in Volumetric CT and MR Images written by Sean Daniel Murphy and published by . This book was released on 2012 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Deep Learning and Medical Applications written by Jin Keun Seo and published by Springer Nature. This book was released on 2023-06-15 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the past 40 years, diagnostic medical imaging has undergone remarkable advancements in CT, MRI, and ultrasound technology. Today, the field is experiencing a major paradigm shift, thanks to significant and rapid progress in deep learning techniques. As a result, numerous innovative AI-based programs have been developed to improve image quality and enhance clinical workflows, leading to more efficient and accurate diagnoses. AI advancements of medical imaging not only address existing unsolved problems but also present new and complex challenges. Solutions to these challenges can improve image quality and reveal new information currently obscured by noise, artifacts, or other signals. Holistic insight is the key to solving these challenges. Such insight may lead to a creative solution only when it is based on a thorough understanding of existing methods and unmet demands. This book focuses on advanced topics in medical imaging modalities, including CT and ultrasound, with the aim of providing practical applications in the healthcare industry. It strikes a balance between mathematical theory, numerical practice, and clinical applications, offering comprehensive coverage from basic to advanced levels of mathematical theories, deep learning techniques, and algorithm implementation details. Moreover, it provides in-depth insights into the latest advancements in dental cone-beam CT, fetal ultrasound, and bioimpedance, making it an essential resource for professionals seeking to stay up-to-date with the latest developments in the field of medical imaging.
Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2023 written by Hayit Greenspan and published by Springer Nature. This book was released on 2023-09-30 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.