Deep learning-based post-processing of real-time MRI to assess and quantify dynamic wrist movement in health and disease

While morphologic magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of ligamentous wrist injuries, it is merely static and incapable of diagnosing dynamic wrist instability. Based on real-time MRI and algorithm-based image post-processing in terms of convolutional neural networks (CNNs), this study aims to develop and validate an automatic technique to quantify wrist movement. A total of 56 bilateral wrists (28 healthy volunteers) were imaged during continuous and alternating maximum ulnar and radial abduction. Following CNN-based automatic segmentations of carpal bone contours, scapholunate and lunotriquetral gap widths were quantified based on dedicated algorithms and as a function of wrist position. Automatic segmentations were in excellent agreement with manual reference segmentations performed by two radiologists as indicated by Dice similarity coefficients of 0.96 ± 0.02 and consistent and unskewed Bland–Altman plots. Clinical applicability of the framework was assessed in a patient with diagnosed scapholunate ligament injury. Considerable increases in scapholunate gap widths across the range-of-motion were found. In conclusion, the combination of real-time wrist MRI and the present framework provides a powerful diagnostic tool for dynamic assessment of wrist function and, if confirmed in clinical trials, dynamic carpal instability that may elude static assessment using clinical-standard imaging modalities

Scalable Exploration of Complex Objects and Environments Beyond Plain Visual Replication​

Digital multimedia content and presentation means are rapidly increasing their sophistication and are now capable of describing detailed representations of the physical world. 3D exploration experiences allow people to appreciate, understand and interact with intrinsically virtual objects. Communicating information on objects requires the ability to explore them under different angles, as well as to mix highly photorealistic or illustrative presentations of the object themselves with additional data that provides additional insights on these objects, typically represented in the form of annotations. Effectively providing these capabilities requires the solution of important problems in visualization and user interaction. In this thesis, I studied these problems in the cultural heritage-computing-domain, focusing on the very common and important special case of mostly planar, but visually, geometrically, and semantically rich objects. These could be generally roughly flat objects with a standard frontal viewing direction (e.g., paintings, inscriptions, bas-reliefs), as well as visualizations of fully 3D objects from a particular point of views (e.g., canonical views of buildings or statues). Selecting a precise application domain and a specific presentation mode allowed me to concentrate on the well defined use-case of the exploration of annotated relightable stratigraphic models (in particular, for local and remote museum presentation). My main results and contributions to the state of the art have been a novel technique for interactively controlling visualization lenses while automatically maintaining good focus-and-context parameters, a novel approach for avoiding clutter in an annotated model and for guiding users towards interesting areas, and a method for structuring audio-visual object annotations into a graph and for using that graph to improve guidance and support storytelling and automated tours. We demonstrated the effectiveness and potential of our techniques by performing interactive exploration sessions on various screen sizes and types ranging from desktop devices to large-screen displays for a walk-up-and-use museum installation. KEYWORDS - Computer Graphics, Human-Computer Interaction, Interactive Lenses, Focus-and-Context, Annotated Models, Cultural Heritage Computing

The Prominence of Artificial Intelligence in COVID-19

In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and we address the issues with our understanding to build a bright and healthy environment.Comment: 63 pages, 3 tables, 17 figure

From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results

Deep Learning for Semantic Segmentation versus Classification in Computational Pathology: Application to mitosis analysis in Breast Cancer grading

Existing computational pathology approaches did not allow, yet, the emergence of effective/efficient computer-aided tools used as a second opinion for pathologists in the daily practice. Focusing on the case of computer-based qualification for breast cancer diagnosis, the present article proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consisted of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing with an F1-score of 94.35%, which is higher than the results from the literature using classical image processing techniques and also higher than the approaches using handcrafted features combined with CNNs. The second approach was an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6 which is higher than the results from the literature using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results showed the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last two chapters; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the described technology.Trabajo de investigació

Design of a High-Speed Architecture for Stabilization of Video Captured Under Non-Uniform Lighting Conditions

Video captured in shaky conditions may lead to vibrations. A robust algorithm to immobilize the video by compensating for the vibrations from physical settings of the camera is presented in this dissertation. A very high performance hardware architecture on Field Programmable Gate Array (FPGA) technology is also developed for the implementation of the stabilization system. Stabilization of video sequences captured under non-uniform lighting conditions begins with a nonlinear enhancement process. This improves the visibility of the scene captured from physical sensing devices which have limited dynamic range. This physical limitation causes the saturated region of the image to shadow out the rest of the scene. It is therefore desirable to bring back a more uniform scene which eliminates the shadows to a certain extent. Stabilization of video requires the estimation of global motion parameters. By obtaining reliable background motion, the video can be spatially transformed to the reference sequence thereby eliminating the unintended motion of the camera. A reflectance-illuminance model for video enhancement is used in this research work to improve the visibility and quality of the scene. With fast color space conversion, the computational complexity is reduced to a minimum. The basic video stabilization model is formulated and configured for hardware implementation. Such a model involves evaluation of reliable features for tracking, motion estimation, and affine transformation to map the display coordinates of a stabilized sequence. The multiplications, divisions and exponentiations are replaced by simple arithmetic and logic operations using improved log-domain computations in the hardware modules. On Xilinx\u27s Virtex II 2V8000-5 FPGA platform, the prototype system consumes 59% logic slices, 30% flip-flops, 34% lookup tables, 35% embedded RAMs and two ZBT frame buffers. The system is capable of rendering 180.9 million pixels per second (mpps) and consumes approximately 30.6 watts of power at 1.5 volts. With a 1024×1024 frame, the throughput is equivalent to 172 frames per second (fps). Future work will optimize the performance-resource trade-off to meet the specific needs of the applications. It further extends the model for extraction and tracking of moving objects as our model inherently encapsulates the attributes of spatial distortion and motion prediction to reduce complexity. With these parameters to narrow down the processing range, it is possible to achieve a minimum of 20 fps on desktop computers with Intel Core 2 Duo or Quad Core CPUs and 2GB DDR2 memory without a dedicated hardware

Towards Data-Driven Large Scale Scientific Visualization and Exploration

Technological advances have enabled us to acquire extremely large datasets but it remains a challenge to store, process, and extract information from them. This dissertation builds upon recent advances in machine learning, visualization, and user interactions to facilitate exploration of large-scale scientific datasets. First, we use data-driven approaches to computationally identify regions of interest in the datasets. Second, we use visual presentation for effective user comprehension. Third, we provide interactions for human users to integrate domain knowledge and semantic information into this exploration process. Our research shows how to extract, visualize, and explore informative regions on very large 2D landscape images, 3D volumetric datasets, high-dimensional volumetric mouse brain datasets with thousands of spatially-mapped gene expression profiles, and geospatial trajectories that evolve over time. The contribution of this dissertation include: (1) We introduce a sliding-window saliency model that discovers regions of user interest in very large images; (2) We develop visual segmentation of intensity-gradient histograms to identify meaningful components from volumetric datasets; (3) We extract boundary surfaces from a wealth of volumetric gene expression mouse brain profiles to personalize the reference brain atlas; (4) We show how to efficiently cluster geospatial trajectories by mapping each sequence of locations to a high-dimensional point with the kernel distance framework. We aim to discover patterns, relationships, and anomalies that would lead to new scientific, engineering, and medical advances. This work represents one of the first steps toward better visual understanding of large-scale scientific data by combining machine learning and human intelligence

Differently stained whole slide image registration technique with landmark validation

Abstract. One of the most significant features in digital pathology is to compare and fuse successive differently stained tissue sections, also called slides, visually. Doing so, aligning different images to a common frame, ground truth, is required. Current sample scanning tools enable to create images full of informative layers of digitalized tissues, stored with a high resolution into whole slide images. However, there are a limited amount of automatic alignment tools handling large images precisely in acceptable processing time. The idea of this study is to propose a deep learning solution for histopathology image registration. The main focus is on the understanding of landmark validation and the impact of stain augmentation on differently stained histopathology images. Also, the developed registration method is compared with the state-of-the-art algorithms which utilize whole slide images in the field of digital pathology. There are previous studies about histopathology, digital pathology, whole slide imaging and image registration, color staining, data augmentation, and deep learning that are referenced in this study. The goal is to develop a learning-based registration framework specifically for high-resolution histopathology image registration. Different whole slide tissue sample images are used with a resolution of up to 40x magnification. The images are organized into sets of consecutive, differently dyed sections, and the aim is to register the images based on only the visible tissue and ignore the background. Significant structures in the tissue are marked with landmarks. The quality measurements include, for example, the relative target registration error, structural similarity index metric, visual evaluation, landmark-based evaluation, matching points, and image details. These results are comparable and can be used also in the future research and in development of new tools. Moreover, the results are expected to show how the theory and practice are combined in whole slide image registration challenges. DeepHistReg algorithm will be studied to better understand the development of stain color feature augmentation-based image registration tool of this study. Matlab and Aperio ImageScope are the tools to annotate and validate the image, and Python is used to develop the algorithm of this new registration tool. As cancer is globally a serious disease regardless of age or lifestyle, it is important to find ways to develop the systems experts can use while working with patients’ data. There is still a lot to improve in the field of digital pathology and this study is one step toward it.Eri menetelmin värjättyjen virtuaalinäytelasien rekisteröintitekniikka kiintopisteiden validointia hyödyntäen. Tiivistelmä. Yksi tärkeimmistä digitaalipatologian ominaisuuksista on verrata ja fuusioida peräkkäisiä eri menetelmin värjättyjä kudosleikkeitä toisiinsa visuaalisesti. Tällöin keskenään lähes identtiset kuvat kohdistetaan samaan yhteiseen kehykseen, niin sanottuun pohjatotuuteen. Nykyiset näytteiden skannaustyökalut mahdollistavat sellaisten kuvien luonnin, jotka ovat täynnä kerroksittaista tietoa digitalisoiduista näytteistä, tallennettuna erittäin korkean resoluution virtuaalisiin näytelaseihin. Tällä hetkellä on olemassa kuitenkin vain kourallinen automaattisia työkaluja, jotka kykenevät käsittelemään näin valtavia kuvatiedostoja tarkasti hyväksytyin aikarajoin. Tämän työn tarkoituksena on syväoppimista hyväksikäyttäen löytää ratkaisu histopatologisten kuvien rekisteröintiin. Tärkeimpänä osa-alueena on ymmärtää kiintopisteiden validoinnin periaatteet sekä eri väriaineiden augmentoinnin vaikutus. Lisäksi tässä työssä kehitettyä rekisteröintialgoritmia tullaan vertailemaan muihin kirjallisuudessa esitettyihin algoritmeihin, jotka myös hyödyntävät virtuaalinäytelaseja digitaalipatologian saralla. Kirjallisessa osiossa tullaan siteeraamaan aiempia tutkimuksia muun muassa seuraavista aihealueista: histopatologia, digitaalipatologia, virtuaalinäytelasi, kuvantaminen ja rekisteröinti, näytteen värjäys, data-augmentointi sekä syväoppiminen. Tavoitteena on kehittää oppimispohjainen rekisteröintikehys erityisesti korkearesoluutioisille digitalisoiduille histopatologisille kuville. Erilaisissa näytekuvissa tullaan käyttämään jopa 40-kertaista suurennosta. Kuvat kudoksista on järjestetty eri menetelmin värjättyihin peräkkäisiin kuvasarjoihin ja tämän työn päämääränä on rekisteröidä kuvat pohjautuen ainoastaan kudosten näkyviin osuuksiin, jättäen kuvien tausta huomioimatta. Kudosten merkittävimmät rakenteet on merkattu niin sanotuin kiintopistein. Työn laatumittauksina käytetään arvoja, kuten kohteen suhteellinen rekisteröintivirhe (rTRE), rakenteellisen samankaltaisuuindeksin mittari (SSIM), sekä visuaalista arviointia, kiintopisteisiin pohjautuvaa arviointia, yhteensopivuuskohtia, ja kuvatiedoston yksityiskohtia. Nämä arvot ovat verrattavissa myös tulevissa tutkimuksissa ja samaisia arvoja voidaan käyttää uusia työkaluja kehiteltäessä. DeepHistReg metodi toimii pohjana tässä työssä kehitettävälle näytteen värjäyksen parantamiseen pohjautuvalle rekisteröintityökalulle. Matlab ja Aperio ImageScope ovat ohjelmistoja, joita tullaan hyödyntämään tässä työssä kuvien merkitsemiseen ja validointiin. Ohjelmointikielenä käytetään Pythonia. Syöpä on maailmanlaajuisesti vakava sairaus, joka ei katso ikää eikä elämäntyyliä. Siksi on tärkeää löytää uusia keinoja kehittää työkaluja, joita asiantuntijat voivat hyödyntää jokapäiväisessä työssään potilastietojen käsittelyssä. Digitaalipatologian osa-alueella on vielä paljon innovoitavaa ja tämä työ on yksi askel eteenpäin taistelussa syöpäsairauksia vastaan

DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks

Crop yield is an essential measure for breeders, researchers and farmers and is comprised of and may be calculated by the number of ears/m2, grains per ear and thousand grain weight. Manual wheat ear counting, required in breeding programmes to evaluate crop yield potential, is labour intensive and expensive; thus, the development of a real-time wheat head counting system would be a significant advancement. In this paper, we propose a computationally efficient system called DeepCount to automatically identify and count the number of wheat spikes in digital images taken under the natural fields conditions. The proposed method tackles wheat spike quantification by segmenting an image into superpixels using Simple Linear Iterative Clustering (SLIC), deriving canopy relevant features, and then constructing a rational feature model fed into the deep Convolutional Neural Network (CNN) classification for semantic segmentation of wheat spikes. As the method is based on a deep learning model, it replaces hand-engineered features required for traditional machine learning methods with more efficient algorithms. The method is tested on digital images taken directly in the field at different stages of ear emergence/maturity (using visually different wheat varieties), with different canopy complexities (achieved through varying nitrogen inputs), and different heights above the canopy under varying environmental conditions. In addition, the proposed technique is compared with a wheat ear counting method based on a previously developed edge detection technique and morphological analysis. The proposed approach is validated with image-based ear counting and ground-based measurements. The results demonstrate that the DeepCount technique has a high level of robustness regardless of variables such as growth stage and weather conditions, hence demonstrating the feasibility of the approach in real scenarios. The system is a leap towards a portable and smartphone assisted wheat ear counting systems, results in reducing the labour involved and is suitable for high-throughput analysis. It may also be adapted to work on RGB images acquired from UAVs