Numerical Methods for Pulmonary Image Registration

Due to complexity and invisibility of human organs, diagnosticians need to analyze medical images to determine where the lesion region is, and which kind of disease is, in order to make precise diagnoses. For satisfying clinical purposes through analyzing medical images, registration plays an essential role. For instance, in Image-Guided Interventions (IGI) and computer-aided surgeries, patient anatomy is registered to preoperative images to guide surgeons complete procedures. Medical image registration is also very useful in surgical planning, monitoring disease progression and for atlas construction. Due to the significance, the theories, methods, and implementation method of image registration constitute fundamental knowledge in educational training for medical specialists. In this chapter, we focus on image registration of a specific human organ, i.e. the lung, which is prone to be lesioned. For pulmonary image registration, the improvement of the accuracy and how to obtain it in order to achieve clinical purposes represents an important problem which should seriously be addressed. In this chapter, we provide a survey which focuses on the role of image registration in educational training together with the state-of-the-art of pulmonary image registration. In the first part, we describe clinical applications of image registration introducing artificial organs in Simulation-based Education. In the second part, we summarize the common methods used in pulmonary image registration and analyze popular papers to obtain a survey of pulmonary image registration

Regmentation: A New View of Image Segmentation and Registration

Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications

Nonparametric image registration of airborne LiDAR, hyperspectral and photographic imagery of wooded landscapes

There is much current interest in using multisensor airborne remote sensing to monitor the structure and biodiversity of woodlands. This paper addresses the application of nonparametric (NP) image-registration techniques to precisely align images obtained from multisensor imaging, which is critical for the successful identification of individual trees using object recognition approaches. NP image registration, in particular, the technique of optimizing an objective function, containing similarity and regularization terms, provides a flexible approach for image registration. Here, we develop a NP registration approach, in which a normalized gradient field is used to quantify similarity, and curvature is used for regularization (NGF-Curv method). Using a survey of woodlands in southern Spain as an example, we show that NGF-Curv can be successful at fusing data sets when there is little prior knowledge about how the data sets are interrelated (i.e., in the absence of ground control points). The validity of NGF-Curv in airborne remote sensing is demonstrated by a series of experiments. We show that NGF-Curv is capable of aligning images precisely, making it a valuable component of algorithms designed to identify objects, such as trees, within multisensor data sets.This work was supported by the Airborne Research and Survey Facility of the U.K.’s Natural Environment Research Council (NERC) for collecting and preprocessing the data used in this research project [EU11/03/100], and by the grants supported from King Abdullah University of Science Technology and Wellcome Trust (BBSRC). D. Coomes was supported by a grant from NERC (NE/K016377/1) and funding from DEFRA and the BBSRC to develop methods for monitoring ash dieback from aircraft.This is the final version. It was first published by IEEE at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7116541&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_Publication_Number%3A36%29%26pageNumber%3D5

Registration of serial sections: An evaluation method based on distortions of the ground truths

Registration of histological serial sections is a challenging task. Serial sections exhibit distortions and damage from sectioning. Missing information on how the tissue looked before cutting makes a realistic validation of 2D registrations extremely difficult. This work proposes methods for ground-truth-based evaluation of registrations. Firstly, we present a methodology to generate test data for registrations. We distort an innately registered image stack in the manner similar to the cutting distortion of serial sections. Test cases are generated from existing 3D data sets, thus the ground truth is known. Secondly, our test case generation premises evaluation of the registrations with known ground truths. Our methodology for such an evaluation technique distinguishes this work from other approaches. Both under- and over-registration become evident in our evaluations. We also survey existing validation efforts. We present a full-series evaluation across six different registration methods applied to our distorted 3D data sets of animal lungs. Our distorted and ground truth data sets are made publicly available.Comment: Supplemental data available under https://zenodo.org/record/428244

Medical image registration by neural networks: a regression-based registration approach

This thesis focuses on the development and evaluation of a registration-by-regression approach for the 3D/2D registration of coronary Computed Tomography Angiography (CTA) and X-ray angiography. This regression-based method relates image features of 2D projection images to the transformation parameters of the 3D image by a nonlinear regression. It treats registration as a regression problem, as an alternative for the traditional iterative approach that often comes with high computational costs and limited capture range. First we presented a survey of the methods with a regression-based registration approach for medical applications, as well as a summary of their main characteristics (Chapter 2). Second, we studied the registration methodology, addressing the input features and the choice of regression model (Chapter 3 and Chapter 4). For that purpose, we evaluated different options using simulated X-ray images generated from coronary artery tree models derived from 3D CTA scans. We also compared the registration-by-regression results with a method based on iterative optimization. Different image features of 2D projections and seven regression techniques were considered. The regression approach for simulated X-rays was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach. Neural Networks obtained accurate results and showed to be robust to large initial misalignment. Third, we evaluated the registration-by-regression method using clinical data, integrating the 3D preoperative CTA of the coronary arteries with intraoperative 2D X-ray angiography images (Chapter 5). For the evaluation of the image registration, a gold standard registration was established using an exhaustive search followed by a multi-observer visual scoring procedure. The influence of preprocessing options for the simulated images and the real X-rays was studied. Several image features were also compared. The coronary registration–by-regression results were not satisfactory, resembling manual initialization accuracy. Therefore, the proposed method for this concrete problem and in its current configuration is not sufficiently accurate to be used in the clinical practice. The framework developed enables us to better understand the dependency of the proposed method on the differences between simulated and real images. The main difficulty lies in the substantial differences in appearance between the images used for training (simulated X-rays from 3D coronary models) and the actual images obtained during the intervention (real X-ray angiography). We suggest alternative solutions and recommend to evaluate the registration-by-regression approach in other applications where training data is available that has similar appearance to the eventual test data