A step-by-step guide to learning iOS app development and exploring the latest Apple development toolsKey FeaturesExplore the latest features of Xcode 11 and the Swift 5 programming language in this ...updated fourth editionKick-start your iOS programming career and have fun building your own iOS appsDiscover the new features of iOS 13 such as Dark Mode, iPad apps for Mac, SwiftUI, and moreBook DescriptioniOS 13 comes with features ranging from Dark Mode and Catalyst through to SwiftUI and Sign In with Apple. If you're a beginner and are looking to experiment and work with these features to create your own apps, then this updated fourth edition gets you off to a strong start. The book offers a comprehensive introduction for programmers who are new to iOS, covering the entire process of learning the Swift language, writing your own apps, and publishing them on the App Store. This edition is updated and revised to cover the new iOS 13 features along with Xcode 11 and Swift 5.
The book starts with an introduction to the Swift programming language, and how to accomplish common programming tasks with it. You'll then start building the user interface (UI) of a complete real-world app, using the latest version of Xcode, and also implement the code for views, view controllers, data managers, and other aspects of mobile apps. The book will then help you apply the latest iOS 13 features to existing apps, along with introducing you to SwiftUI, a new way to design UIs. Finally, the book will take you through setting up testers for your app, and what you need to do to publish your app on the App Store.
By the end of this book, you'll be well versed with how to write and publish apps, and will be able to apply the skills you've gained to enhance your apps.What you will learnGet to grips with the fundamentals of Xcode 11 and Swift 5, the building blocks of iOS developmentUnderstand how to prototype an app using storyboardsDiscover the Model-View-Controller design pattern, and how to implement the desired functionality within the appImplement the latest iOS features such as Dark Mode and Sign In with AppleUnderstand how to convert an existing iPad app into a Mac appDesign, deploy, and test your iOS applications with industry patterns and practicesWho this book is forThis book is for anyone who has programming experience but is completely new to Swift and iOS app development. Experienced programmers looking to explore the latest iOS 13 features will also find this book useful.
•A novel multi-scale deep learning based registration framework that leverages global and local information for estimation of non-linear deformation fields.•A difficulty-aware module is incorporated ...to identify hard-to-register image regions for deformation refinement..•The registration framework consists of a cascade of neural networks to progressively refine the deformation field in a coarse-to-fine manner.•A dynamic anti-folding penalization to penalize large deformations that cause folding and tearing.•Extensive experiments conducted on four public datasets validate that our method improves registration accuracy with better preservation of topology.
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The aim of deformable brain image registration is to align anatomical structures, which can potentially vary with large and complex deformations. Anatomical structures vary in size and shape, requiring the registration algorithm to estimate deformation fields at various degrees of complexity. Here, we present a difficulty-aware model based on an attention mechanism to automatically identify hard-to-register regions, allowing better estimation of large complex deformations. The difficulty-aware model is incorporated into a cascaded neural network consisting of three sub-networks to fully leverage both global and local contextual information for effective registration. The first sub-network is trained at the image level to predict a coarse-scale deformation field, which is then used for initializing the subsequent sub-network. The next two sub-networks progressively optimize at the patch level with different resolutions to predict a fine-scale deformation field. Embedding difficulty-aware learning into the hierarchical neural network allows harder patches to be identified in the deeper sub-networks at higher resolutions for refining the deformation field. Experiments conducted on four public datasets validate that our method achieves promising registration accuracy with better preservation of topology, compared with state-of-the-art registration methods.
In this paper, we present a new multimodal image registration technique established on elastodynamics notion. The main idea behind this concept is the progression of waves on an elastic body as soon ...as it is disturbed from its initial rest state. We propose to solve the multimodal registration problem by modeling the non-linear deformations as elastic waves and iteratively solving the elastodynamics wave equation to estimate the transformation. The inertial force in elastodynamics model is computed as the gradient of mutual information which considers the statistical relationship between the intensities of the images acquired using different imaging modalities. We tested our method on T1–T2 weighted MR brain image pairs and MR-CT brain image pairs. The proposed registration technique was compared against a variant of demons method proposed for multimodal images. The registration results were analyzed by examining the overlay images and by computing the normalized mutual information. The qualitative and quantitative analysis proved that our proposed method registers the images better than the compared method.
Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated ...convolution implementation and deep supervision. Such complex networks need large training datasets, a requirement which is challenging for medical image analysis. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image semantic segmentation, (2) prediction of the instance class labels of the objects within the image, and (3) classification of the scene the image belonging to. While these three sub-tasks are trained to optimize their individual loss functions at different perceptual levels, we propose to allow their interaction within the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise semantic segmentation and the instance class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate segmentation, even though the number of images for training may be limited in many biomedical applications. We have successfully applied our framework to three diverse 2D/3D medical image datasets, including Robotic Scene Segmentation Challenge 18 (ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus Glaucoma Challenge (REFUGE18). We have achieved outperformed or comparable performance in all the three challenges. Our code, typical data and trained models are available at https://github.com/xuhuaren/TDSNet.
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