In this thesis, we tackle learning-based multi-modal image registration. Multi-modal registration, in which two images of dierent modalities need to be aligned to each other, is a difficult yet essential task for medical imaging analysis. Classical methods have been developed for single-modal and multi-modal registration, but are slow because they solve an optimization problem for each pair of images. Recently, deep learning methods for registration have been proposed, and have been shown to shorten registration time by learning a global function to perform registration, which can then be applied quickly on a pair of test images. These methods perform well for single-modal registration but have not yet been extended to the harder task of multi-modal registration. We bridge this gap by implementing classical multi-modal metrics in a differentiable and efficient manner to enable deep image registration for multi-modal data. We nd that our method for multi-modal registration performs significantly better than baselines, in terms of both accuracy and runtime.
Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid
This book constitutes the refereed proceedings of the 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019, held in Liverpool, UK, in July 2019. The 43 full papers presented were carefully reviewed and selected from 70 submissions. There were organized in topical sections named: oncology and tumour imaging; lesion, wound and ulcer analysis; biostatistics; fetal imaging; enhancement and reconstruction; diagnosis, classification and treatment; vessel and nerve analysis; image registration; image segmentation; ophthalmic imaging; and posters.
This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
Currently there is no shortage of challenges and opportunities for PET instrumentation and quantitative imaging techniques. Molecular imaging technologies are leading to a revolutionary paradigm shift in health care and clinical practice. This issue reviews recent advances in PET instrumentation as well as volume correction strategies and strategies for motion tracking and correction, scatter compensation techniques, and attenuation correction techniques.
Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache
Multi-modal retinal images provide complementary anatomical information at various resolutions, color wavelengths, and fields of view. Aligning multi-modal images will establish a comprehensive view of the retina and benefit the screening and diagnosis of eye diseases. However, the inconsistent anatomical patterns across modalities create outliers in feature matching, and the lack of retinal boundaries may also fool the intensity-based alignment metrics, both of which will influence the alignment qualities. Besides, the varying distortion levels across Ultra-Widefield (UWF) and Narrow-Angle (NA) images, due to different camera parameters, will cause large alignment errors in global transformation. In addressing the issue of inconsistent patterns, we use retinal vasculature as a common signal for alignment. First, we build a two-step coarse-to-fine registration pipeline fully based on deep neural networks. The coarse alignment step estimates a global transformation via vessel segmentation, feature detection and description, and outlier rejection. While the fine alignment step corrects the remaining misalignment through deformable registration. In addition, we propose an unsupervised learning scheme based on style transfer to jointly train the networks for vessel segmentation and deformable registration. Finally, we also introduce Monogenical Phase signal as an alternative guidance in training the deformable registration network. Then, to deal with the issue of various distortion levels across UWF and NA modalities, we propose a distortion correction function to create images with similar distortion levels. Based on the assumptions of spherical eyeball shape and fixed UWF camera pose, the function reprojects the UWF pixels by an estimated correction camera with similar parameters as the NA camera. Besides, we incorporate the function into the coarse alignment networks which will simultaneously optimize the correction camera pose and refine the global alignment results. Moreover, to further reduce misalignment from the UWF-to-NA global registration, we estimate a 3D dense scene for the UWF pixels to represent a more flexible eyeball shape. Both the scene and the NA camera parameters are iteratively optimized to reduce the alignment error between the 3D-to-2D reprojected images and the original ones, which is also concatenated with the coarse alignment networks with distortion correction function.
The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
