Similarity regularized sparse group lasso for cup to disc ratio computation

© 2017 Optical Society of America. Automatic cup to disc ratio (CDR) computation from color fundus images has shown to be promising for glaucoma detection. Over the past decade, many algorithms have been proposed. In this paper, we first review the recent work in the area and then present a novel similarity-regularized sparse group lasso method for automated CDR estimation. The proposed method reconstructs the testing disc image based on a set of reference disc images by integrating the similarity between testing and the reference disc images with the sparse group lasso constraints. The reconstruction coefficients are then used to estimate the CDR of the testing image. The proposed method has been validated using 650 images with manually annotated CDRs. Experimental results show an average CDR error of 0.0616 and a correlation coefficient of 0.7, outperforming other methods. The areas under curve in the diagnostic test reach 0.843 and 0.837 when manual and automatically segmented discs are used respectively, better than other methods as well

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Data efficient deep learning for medical image analysis: A survey

The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.Comment: Under Revie

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

Image Quality Assessment for Population Cardiac MRI: From Detection to Synthesis

Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Left Ventricular (LV) cardiac anatomy and function are widely used for diagnosis and monitoring disease progression in cardiology and to assess the patient's response to cardiac surgery and interventional procedures. For population imaging studies, CMR is arguably the most comprehensive imaging modality for non-invasive and non-ionising imaging of the heart and great vessels and, hence, most suited for population imaging cohorts. Due to insufficient radiographer's experience in planning a scan, natural cardiac muscle contraction, breathing motion, and imperfect triggering, CMR can display incomplete LV coverage, which hampers quantitative LV characterization and diagnostic accuracy. To tackle this limitation and enhance the accuracy and robustness of the automated cardiac volume and functional assessment, this thesis focuses on the development and application of state-of-the-art deep learning (DL) techniques in cardiac imaging. Specifically, we propose new image feature representation types that are learnt with DL models and aimed at highlighting the CMR image quality cross-dataset. These representations are also intended to estimate the CMR image quality for better interpretation and analysis. Moreover, we investigate how quantitative analysis can benefit when these learnt image representations are used in image synthesis. Specifically, a 3D fisher discriminative representation is introduced to identify CMR image quality in the UK Biobank cardiac data. Additionally, a novel adversarial learning (AL) framework is introduced for the cross-dataset CMR image quality assessment and we show that the common representations learnt by AL can be useful and informative for cross-dataset CMR image analysis. Moreover, we utilize the dataset invariance (DI) representations for CMR volumes interpolation by introducing a novel generative adversarial nets (GANs) based image synthesis framework, which enhance the CMR image quality cross-dataset

A study of the genus Thaumetopoea (Lepidoptera: Notodontidae), using morphological, ecological, andmolecular traits

Thaumetopoeinae include several species called processionary moths, mainly due to their gregarious behaviour to form long single lines to forage and to pupate. Even if historically it has been considered as a separate family, Thaumetopoeinae were recently included as a subfamily of Notodontidae, based on both cladistic and molecular analyses. This groups has a great importance for forestry and landscape because their larvae feed on trees and shrubs, both broadleaved and coniferous, defoliating the canopy and weakening the plants, making them more susceptible to tree killers (i.e. bark beetles). Furthermore, they threaten human and animal health due to the presence of urticating setae in larvae and/or adults that are used as an effective defence strategy against vertebrate predators. In humans, these setae are responsible of allergic reactions, also of strong intensity, from dermatitis to anaphylactic shock; instead in animals, they produce pruritus, necrosis, abortions, anorexia, up to death. Thaumetopoea is the most known genus and historically it has been split into three separated genera. A recent molecular study has defined the phylogenetic relationships and the evolution of life history traits for a few taxa of this genus that is mainly distributed in Europe, the Mediterranean and Iranoturanic areas. In the first study, I completed the phylogeny of the genus analysing all the 15 species of Thaumetopoea s. lat., introducing both rare species found in different museum collections, by defining a set of 165 traits from head, thorax, abdomen, wing and male genitalia. The very recently described Thaumetopoea loxostigma Hacker, 2016 could not be included as the only extant specimen, viz. the holotype, was not available. According to the original description, T. loxostigma is closely related to the Thaumetopoea apologetica – Thaumetopoea jordana group, which itself needs revision. Five subspecies other than nominal ones are currently recognised within Thaumetopoea , namely T. apologetica abyssinica, T. herculeana judaea, T. processionea pseudosolitaria, T. solitaria iranica, and T. pityocampa orana, and they were included in the analysis. Whenever possible, the specimens were compared with the types of the various species. Morphological traits were combined with 9 mitochondrial genes already present in literature for some species. For the others, I sequenced the barcoding portion of cox1 due to the difficulties to amplify old fragmented DNA also using ad hoc primers. Matrix was processed using different software, to test different approaches, and the analyses were conducted both on separated matrix (morphological vs. molecular) and in combined ones (morphological + molecular). Finally, morphological traits were plotted on reference tree in order to identify apomorphies and homoplasious changes useful to draw a morphological key for the Thaumetopoea genus. Furthermore, I selected ecological and life history traits: presence of urticating setae on larva; pupation site; larval seasonal feeding activity; host plant group; host plant family, in order to outline the traits of a possible ancestor of processionary moths. In the second study, I use the morphological traits collected and the large number of specimens analysed to draw a morphological key and distribution maps of the whole genus Thaumetopoea, which will be helpful to entomologists and foresters to identify the adult specimens both in museum collection and in field. In the meantime, I synonymised some taxa recently described, mainly for lack of diagnostic characters or inconsistency. In the third study, I focused on the single clade genus Thaumetopoea in order to collect the information about the species of the 'summer' Thaumetopoea from Eurasia, feeding on coniferous hosts. Information included morphological and life history traits. Furthermore, the work involved also T. cheela that is proposed to be included in this group based on morphology and indirect evidence of life history traits. Although some evidences supported the hypothesis that Cedrus could be the host on which most of speciation in the summer clade had happened, more studies have to be made, especially for the less known species. Although my works complete the phylogeny of the genus of Thaumetopoea and provide valid methods to identify the species, which is very important because of the group includes some of the most important forest pests that also affect human and domesticated animal health through the urticating setae, more work is left to do in order to complete the knowledge on some neglected taxa and to expand the analysis to other genera of the subfamily, which apparently share the same traits and are causing similar problems in other continents

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas