Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification... more
Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86{\%} (with the YOLO network) for detection and 99.51{\%} for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatoms.
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High-resolution images of phytoplankton cells such as diatoms or desmids, which are useful for monitoring water quality, can now be provided by digital microscopes, facilitating the automated analysis and identification of specimens.... more
High-resolution images of phytoplankton cells such as diatoms or desmids, which are useful for monitoring water quality, can now be provided by digital microscopes, facilitating the automated analysis and identification of specimens. Conventional approaches are based on optical microscopy; however, manual image analysis is impractical due to the huge diversity of this group of microalgae and its great morphological plasticity. As such, there is a need for automated recognition techniques for diagnostic tools (e.g. environmental monitoring networks, early warning systems) to improve the management of water resources and decision-making processes. Describing the entire workflow of a bioindicator system, from capture, analysis and identification to the determination of quality indices, this book provides insights into the current state-of-the-art in automatic identification systems in microscopy.
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Introduction/ Background In recent years different technological solutions have emerged for the scanning or digitization of histological and cytological slides in pathology, from several manufacturers. High resolution scanning is usually... more
Introduction/ Background In recent years different technological solutions have emerged for the scanning or digitization of histological and cytological slides in pathology, from several manufacturers. High resolution scanning is usually based in tile (small fragment) or stripes (longitudinal areas) that are combined or stitched together to create a high magnification (usually equivalent to 20x to 40x magnification) global image. Thus, a large digital slide can be displayed using specific viewers to simulate the functions of a conventional microscope. A pyramid of images is a common solution. But each scanner manufacturer optimizes the process of collecting, managing and storing images in its own format, making difficult the interconnection between systems and the ability to share images between different formats, and, generally a heavy process of image conversion and a loss of information is needed. Aims The objective is to present the process performed on proprietary image formats...
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Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification... more
Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different...
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Diatoms are unicellular algae present almost wherever there is water. Diatom identification has many applications in different fields of study, such as ecology, forensic science, etc. In environmental studies, algae can be used as a... more
Diatoms are unicellular algae present almost wherever there is water. Diatom identification has many applications in different fields of study, such as ecology, forensic science, etc. In environmental studies, algae can be used as a natural water quality indicator. The diatom life cycle consists of the set of stages that pass through the successive generations of each species from the initial to the senescent cells. Life cycle modeling is a complex process since in general the distribution of the parameter vectors that represent the variations that occur in this process is non-linear and of high dimensionality. In this paper, we propose to characterize the diatom life cycle by the main features that change during the algae life cycle, mainly the contour shape and the texture. Elliptical Fourier Descriptors (EFD) are used to describe the diatom contour while phase congruency and Gabor filters describe the inner ornamentation of the algae. The proposed method has been tested with a sm...
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Research Interests: Materials Engineering, Computer Science, Artificial Intelligence, Condensed Matter Physics, Microscopy, and 10 moreMedicine, Linear Discriminant Analysis, Pollen, Texture Analysis, Surface Properties, Gray Level Co-occurrence Matrix, Interdisciplinary Engineering, Laboratory Automation, Local Binary Patterns, and Micron
Research Interests: Computer Science, Algorithms, Artificial Intelligence, Computer Vision, Biomedical Engineering, and 15 moreMicroscopy, Medicine, Biomedical Optics, Brain, Humans, Best practice, Male, Light, Lung, Optical physics, High performance, Optometry and Ophthalmology, Evaluation Studies, Autofocus, and Field of View
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In recent scientific literature, some studies have been published where recognition rates obtained with Deep Learning (DL) surpass those obtained by humans on the same task. In contrast to this, other studies have shown that DL networks... more
In recent scientific literature, some studies have been published where recognition rates obtained with Deep Learning (DL) surpass those obtained by humans on the same task. In contrast to this, other studies have shown that DL networks have a somewhat strange behavior which is very different from human responses when confronted with the same task. The case of the so-called “adversarial examples” is perhaps the best example in this regard. Despite the biological plausibility of neural networks, the fact that they can produce such implausible misclassifications still points to a fundamental difference between human and machine learning. This paper delves into the possible causes of this intriguing phenomenon. We first contend that, if adversarial examples are pointing to an implausibility it is because our perception of them relies on our capability to recognise the classes of the images. For this reason we focus on what we call cognitively adversarial examples, which are those obtained from samples that the classifier can in fact recognise correctly. Additionally, in this paper we argue that the phenomenon of adversarial examples is rooted in the inescapable trade-off that exists in machine learning (including DL) between fitting and generalization. This hypothesis is supported by experiments carried out in which the robustness to adversarial examples is measured with respect to the degree of fitting to the training samples.
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The phenomenon of Adversarial Examples has become one of the most intriguing topics associated to deep learning. The so-called adversarial attacks have the ability to fool deep neural networks with inappreciable perturbations. While the... more
The phenomenon of Adversarial Examples has become one of the most intriguing topics associated to deep learning. The so-called adversarial attacks have the ability to fool deep neural networks with inappreciable perturbations. While the effect is striking, it has been suggested that such carefully selected injected noise does not necessarily appear in real-world scenarios. In contrast to this, some authors have looked for ways to generate adversarial noise in physical scenarios (traffic signs, shirts, etc.), thus showing that attackers can indeed fool the networks. In this paper we go beyond that and show that adversarial examples also appear in the real-world without any attacker or maliciously selected noise involved. We show this by using images from tasks related to microscopy and also general object recognition with the well-known ImageNet dataset. A comparison between these natural and the artificially generated adversarial examples is performed using distance metrics and imag...
Objectives. The present study aimed to examine the sedentary behavior (SB) and physical activity (PA) levels in children using six selected accelerometry protocols based on diverse cut-off points.Methods. Clinical examination,... more
Objectives. The present study aimed to examine the sedentary behavior (SB) and physical activity (PA) levels in children using six selected accelerometry protocols based on diverse cut-off points.Methods. Clinical examination, anthropometric measurements, and PA evaluation by accelerometry were assessed in 543 selected children (10±2.4 years old) from the Spanish GENOBOX study. The ActiLife data scoring program was used to determine daily min spent in SB, and light, moderate, vigorous and moderate-vigorous PA using six validated accelerometry protocols differing in their cutt-off points.Results. Very different estimations for SB and PA intensity levels were found in children, independently of the non-wear-time algorithm selected, and considering puberty stages, age and body mass index. The time spent in daily SB varied from 471 to 663.7 min, PA ranged from 141 to 301.6 min, and the moderate-vigorous PA was reported between 20.7 and 180.2 min.Conclusion. The choice of a particular ac...
Adversarial examples are one of the most intriguing topics in modern deep learning. Imperceptible perturbations to the input can fool robust models. In relation to this problem, attack and defense methods are being developed almost on a... more
Adversarial examples are one of the most intriguing topics in modern deep learning. Imperceptible perturbations to the input can fool robust models. In relation to this problem, attack and defense methods are being developed almost on a daily basis. In parallel, efforts are being made to simply pointing out when an input image is an adversarial example. This can help prevent potential issues, as the failure cases are easily recognizable by humans. The proposal in this work is to study how chaos theory methods can help distinguish adversarial examples from regular images. Our work is based on the assumption that deep networks behave as chaotic systems, and adversarial examples are the main manifestation of it (in the sense that a slight input variation produces a totally different output). In our experiments, we show that the Lyapunov exponents (an established measure of chaoticity), which have been recently proposed for classification of adversarial examples, are not robust to image...
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Ki67 hot-spot detection and its evaluation in invasive breast cancer regions play a significant role in routine medical practice. The quantification of cellular proliferation assessed by Ki67 immunohistochemistry is an established... more
Ki67 hot-spot detection and its evaluation in invasive breast cancer regions play a significant role in routine medical practice. The quantification of cellular proliferation assessed by Ki67 immunohistochemistry is an established prognostic and predictive biomarker that determines the choice of therapeutic protocols. In this paper, we present three deep learning-based approaches to automatically detect and quantify Ki67 hot-spot areas by means of the Ki67 labeling index. To this end, a dataset composed of 100 whole slide images (WSIs) belonging to 50 breast cancer cases (Ki67 and H&E WSI pairs) was used. Three methods based on CNN classification were proposed and compared to create the tumor proliferation map. The best results were obtained by applying the CNN to the mutual information acquired from the color deconvolution of both the Ki67 marker and the H&E WSIs. The overall accuracy of this approach was 95%. The agreement between the automatic Ki67 scoring and the manual analysis...
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Deep learning (henceforth DL) has become most powerful machine learning methodology. Under specific circumstances recognition rates even surpass those obtained by humans. Despite this, several works have shown that deep learning produces... more
Deep learning (henceforth DL) has become most powerful machine learning methodology. Under specific circumstances recognition rates even surpass those obtained by humans. Despite this, several works have shown that deep learning produces outputs that are very far from human responses when confronted with the same task. This the case of the so-called “adversarial examples” (henceforth AE). The fact that such implausible misclassifications exist points to a fundamental difference between machine and human learning. This paper focuses on the possible causes of this intriguing phenomenon. We first argue that the error in adversarial examples is caused by high bias, i.e. by regularization that has local negative effects. This idea is supported by our experiments in which the robustness to adversarial examples is measured with respect to the level of fitting to training samples. Higher fitting was associated to higher robustness to adversarial examples. This ties the phenomenon to the tra...
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Neuroblastoma is the most common extra-cranial solid pediatric cancer and causes approximately 15% of all childhood deaths from cancer. Although lymphatic vasculature is a prerequisite for the maintenance of tissue fluid balance and... more
Neuroblastoma is the most common extra-cranial solid pediatric cancer and causes approximately 15% of all childhood deaths from cancer. Although lymphatic vasculature is a prerequisite for the maintenance of tissue fluid balance and immunity in the body, little is known about the relationship between lymphatic vascularization and prognosis in neuroblastoma. We used our previously-published custom-designed tool to close open-outline vessels and measure the density, size and shape of all lymphatic vessels and microvascular segments in 332 primary neuroblastoma contained in tissue microarrays. The results were correlated with clinical and biological features of known prognostic value and with risk of progression to establish histological lymphatic vascular patterns associated with unfavorable histology. A high proportion of irregular intermediate lymphatic capillaries and irregular small collector vessels were present in tumors from patients with metastatic stage, undifferentiating neu...
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We study the effectiveness of several low-cost oblique illumination filters to improve overall image quality, in comparison with standard bright field imaging. For this purpose, a dataset composed of 3360 diatom images belonging to 21... more
We study the effectiveness of several low-cost oblique illumination filters to improve overall image quality, in comparison with standard bright field imaging. For this purpose, a dataset composed of 3360 diatom images belonging to 21 taxa was acquired. Subjective and objective image quality assessments were done. The subjective evaluation was performed by a group of diatom experts by psychophysical test where resolution, focus, and contrast were assessed. Moreover, some objective nonreference image quality metrics were applied to the same image dataset to complete the study, together with the calculation of several texture features to analyze the effect of these filters in terms of textural properties. Both image quality evaluation methods, subjective and objective, showed better results for images acquired using these illumination filters in comparison with the no filtered image. These promising results confirm that this kind of illumination filters can be a practical way to impro...
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Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and... more
Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting. Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constr...
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Many biological objects are barely distinguished with the brightfield microscope because they appear transparent, translucent and colourless. One simple way to make such specimens visible without compromising contrast and resolution is by... more
Many biological objects are barely distinguished with the brightfield microscope because they appear transparent, translucent and colourless. One simple way to make such specimens visible without compromising contrast and resolution is by controlling the amount and the directionality of the illumination light. Oblique illumination is an old technique described by many scientists and microscopists that however has been largely neglected in favour of other alternative methods. Oblique lighting (OL) is created by illuminating the sample by only a portion of the light coming from the condenser. If properly used it can improve the resolution and contrast of transparent specimens such as diatoms. In this paper a quantitative evaluation of OL in brigthfield microscopy is presented. Several feature descriptors were selected for characterising contrast and sharpness showing that in general OL provides better performance for distinguishing minute details compared to other lighting modalities....
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Digital pathology is an interdisciplinary field where competency in pathology, laboratory techniques, informatics, computer science, information systems, engineering, and even biology converge. This implies that teaching students about... more
Digital pathology is an interdisciplinary field where competency in pathology, laboratory techniques, informatics, computer science, information systems, engineering, and even biology converge. This implies that teaching students about digital pathology requires coverage, expertise, and hands-on experience in all these disciplines. With this in mind, a syllabus was developed for a digital pathology summer school aimed at professionals in the aforementioned fields, as well as trainees and doctoral students. The aim of this communication is to share the context, rationale, and syllabus for this school of digital pathology.
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Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised... more
Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring. The contest dataset comprised of digitised whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The conte...
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Immunohistochemical (IHC) biomarkers in breast tissue microarray (TMA) samples are used daily in pathology departments. In recent years, automatic methods to evaluate positive staining have been investigated since they may save time and... more
Immunohistochemical (IHC) biomarkers in breast tissue microarray (TMA) samples are used daily in pathology departments. In recent years, automatic methods to evaluate positive staining have been investigated since they may save time and reduce errors in the diagnosis. These errors are mostly due to subjective evaluation. The aim of this work is to develop a density tool able to automatically quantify the positive brown IHC stain in breast TMA for different biomarkers. To avoid the problem of colour variation and make a robust tool independent of the staining process, several colour standardization methods have been analysed. Four colour standardization methods have been compared against colour model segmentation. The standardization methods have been compared by means of NBS colour distance. The use of colour standardization helps to reduce noise due to stain and histological sample preparation. However, the most reliable and robust results have been obtained by combining the HSV an...
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The work from Randell et al.[1] evaluates the efficiency of pathologists when using different technologies for interpreting microscopic slides: A conventional microscope, a 24-inch 2.3-megapixel single-screen display (1920 × 1200 pixels),... more
The work from Randell et al.[1] evaluates the efficiency of pathologists when using different technologies for interpreting microscopic slides: A conventional microscope, a 24-inch 2.3-megapixel single-screen display (1920 × 1200 pixels), and a three-screen 11-megapixel display consisting of three 27-inch 3.6-megapixel displays (each 1440 × 2560 pixels). They measured time to detect areas of interest, time to diagnosis, and user confidence. With the participation of nine consultant pathologists, each of them reviewing three slides with each of the aforementioned technologies, a total number of 81 different slides were used.
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A complete image recognition interpretation systemor vision system, usually consists of three levels, low, mid and high level. The ultimate aim is to use data contained in the image to recognize and interpret the processed information... more
A complete image recognition interpretation systemor vision system, usually consists of three levels, low, mid and high level. The ultimate aim is to use data contained in the image to recognize and interpret the processed information available. There are problems of transforming the image into meaningful data and symbols (image based measurement) to achieve detection or classification of an object. This may also include parameter estimation, (size, position, orientation of an object) or shape analysis. The task r~quirements to represent and recognize objects in CT medical images is analyzed, in terms of the information that is necessary (i.e, how to represent the information, what kind of paradigms can be used to process this information and how to reconstruct the objects). In theory object classification (or estimation of parameters of the object) could be accomplished by direct application of the established statistical theory to the image. A good summary of the established statistical techniques may be found in Fig 1 . Unfortunately, there are limitations; usually the relation between objects and image is too complex to describe with conditional probability densities. The number of pixels in a typical image ranges between 512*512,256*256 and 512*512*2 bytes/pixel. Consequently the dimension of a measurement vector can become excessive and the direct processing of such a vector with statistical techniques is often impractical. In recognition of this, it is proposed that one method of overcoming such limitations is to make use of syntactic or structural pattern recognition or combinations of the desirable features of each approach leading to hybrid techniques as potential solutions.
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... Various restoration techniques have, therefore, been developed to compensate for the effects of degradation. ... Bioinformatics Laboratory of School of Computing Science at UEA has been working on the advanced development of... more
... Various restoration techniques have, therefore, been developed to compensate for the effects of degradation. ... Bioinformatics Laboratory of School of Computing Science at UEA has been working on the advanced development of intelligent deconvolution algorithms based on ...
Exploit the amazing features of OpenCV to create powerful image processing applications through easy-to-follow examples About This BookLearn how to build full-fledged image processing applications using free tools and librariesTake... more
Exploit the amazing features of OpenCV to create powerful image processing applications through easy-to-follow examples About This BookLearn how to build full-fledged image processing applications using free tools and librariesTake advantage of cutting-edge image processing functionalities included in OpenCV v3Understand and optimize various features of OpenCV with the help of easy-to-grasp examplesWho This Book Is ForIf you are a competent C++ programmer and want to learn the tricks of image processing with OpenCV, then this book is for you. A basic understanding of image processing is required. In Detail OpenCV, arguably the most widely used computer vision library, includes hundreds of ready-to-use imaging and vision functions and is used in both academia and enterprises.This book provides an example-based tour of OpenCV's main image processing algorithms. Starting with an exploration of library installation, wherein the library structure and basics of image and video reading/writing are covered, you will dive into image filtering and the color manipulation features of OpenCV with LUTs. You'll then be introduced to techniques such as inpainting and denoising to enhance images as well as the process of HDR imaging. Finally, you'll master GPU-based accelerations. By the end of this book, you will be able to create smart and powerful image processing applications with ease! All the topics are described with short, easy-to-follow examples.
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Breast cancer is the most common type of cancer and the fifth leading cause of death in women over 40. Therefore, prompt diagnostic and treatment is essential. In this work a TMA Computer Aided Diagnosis (CAD) system has been implemented... more
Breast cancer is the most common type of cancer and the fifth leading cause of death in women over 40. Therefore, prompt diagnostic and treatment is essential. In this work a TMA Computer Aided Diagnosis (CAD) system has been implemented to provide support to pathologists in their daily work. For that purpose, the tool covers each and every process from the TMA core image acquisition to their individual classification. The first process includes: tissue core location, segmentation and rigid registration of digital microscopic images acquired at different magnifications (5x, 10x, 20x, 20x and 40x) from different devices. The classification process allows performing the core classification selecting different types of color models, texture descriptors and classifiers. Finally, the cores are classified into three categories: malignant, doubtful and benign.
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Breast cancer is the most common type of cancer and the fifth leading cause of death in women over 40. Therefore, prompt diagnostic and treatment is essential. In this work a TMA Computer Aided Diagnosis (CAD) system has been implemented... more
Breast cancer is the most common type of cancer and the fifth leading cause of death in women over 40. Therefore, prompt diagnostic and treatment is essential. In this work a TMA Computer Aided Diagnosis (CAD) system has been implemented to provide support to pathologists in their daily work. For that purpose, the tool covers each and every process from the TMA core image acquisition to their individual classification. The first process includes: tissue core location, segmentation and rigid registration of digital microscopic images acquired at different magnifications (5x, 10x, 20x, 20x and 40x) from different devices. The classification process allows performing the core classification selecting different types of color models, texture descriptors and classifiers. Finally, the cores are classified into three categories: malignant, doubtful and benign.