Class-Agnostic Counting

Nearly all existing counting methods are designed for a specific object class. Our work, however, aims to create a counting model able to count any class of object. To achieve this goal, we formulate counting as a matching problem, enabling us to exploit the image self-similarity property that naturally exists in object counting problems. We make the following three contributions: first, a Generic Matching Network (GMN) architecture that can potentially count any object in a class-agnostic manner; second, by reformulating the counting problem as one of matching objects, we can take advantage of the abundance of video data labeled for tracking, which contains natural repetitions suitable for training a counting model. Such data enables us to train the GMN. Third, to customize the GMN to different user requirements, an adapter module is used to specialize the model with minimal effort, i.e. using a few labeled examples, and adapting only a small fraction of the trained parameters. This is a form of few-shot learning, which is practical for domains where labels are limited due to requiring expert knowledge (e.g. microbiology). We demonstrate the flexibility of our method on a diverse set of existing counting benchmarks: specifically cells, cars, and human crowds. The model achieves competitive performance on cell and crowd counting datasets, and surpasses the state-of-the-art on the car dataset using only three training images. When training on the entire dataset, the proposed method outperforms all previous methods by a large margin.Comment: Asian Conference on Computer Vision (ACCV), 201

Automatic generation of synthetic retinal fundus images:Vascular network

AbstractThis work is part of an ongoing project aimed to generate synthetic retinal fundus images. This paper concentrates on the generation of synthetic vascular networks with realistic shape and texture characteristics. An example-based method, the Active Shape Model, is used to synthesize reliable vessels’ shapes. An approach based on Kalman Filtering combined with an extension of the Multiresolution Hermite vascular cross-section model has been developed for the simulation of vessels’ textures. The proposed method is able to generate realistic synthetic vascular networks with morphological properties that guarantee the correct flow of the blood and the oxygenation of the retinal surface observed by fundus cameras. The validity of our synthetic retinal images is demonstrated by qualitative assessment and quantitative analysis

Simulation of microarray data with realistic characteristics

BACKGROUND: Microarray technologies have become common tools in biological research. As a result, a need for effective computational methods for data analysis has emerged. Numerous different algorithms have been proposed for analyzing the data. However, an objective evaluation of the proposed algorithms is not possible due to the lack of biological ground truth information. To overcome this fundamental problem, the use of simulated microarray data for algorithm validation has been proposed. RESULTS: We present a microarray simulation model which can be used to validate different kinds of data analysis algorithms. The proposed model is unique in the sense that it includes all the steps that affect the quality of real microarray data. These steps include the simulation of biological ground truth data, applying biological and measurement technology specific error models, and finally simulating the microarray slide manufacturing and hybridization. After all these steps are taken into account, the simulated data has realistic biological and statistical characteristics. The applicability of the proposed model is demonstrated by several examples. CONCLUSION: The proposed microarray simulation model is modular and can be used in different kinds of applications. It includes several error models that have been proposed earlier and it can be used with different types of input data. The model can be used to simulate both spotted two-channel and oligonucleotide based single-channel microarrays. All this makes the model a valuable tool for example in validation of data analysis algorithms

A novel neural network approach to cDNA microarray image segmentation

This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p

Analysis of Spatial Point Patterns in Nuclear Biology

There is considerable interest in cell biology in determining whether, and to what extent, the spatial arrangement of nuclear objects affects nuclear function. A common approach to address this issue involves analyzing a collection of images produced using some form of fluorescence microscopy. We assume that these images have been successfully pre-processed and a spatial point pattern representation of the objects of interest within the nuclear boundary is available. Typically in these scenarios, the number of objects per nucleus is low, which has consequences on the ability of standard analysis procedures to demonstrate the existence of spatial preference in the pattern. There are broadly two common approaches to look for structure in these spatial point patterns. First a spatial point pattern for each image is analyzed individually, or second a simple normalization is performed and the patterns are aggregated. In this paper we demonstrate using synthetic spatial point patterns drawn from predefined point processes how difficult it is to distinguish a pattern from complete spatial randomness using these techniques and hence how easy it is to miss interesting spatial preferences in the arrangement of nuclear objects. The impact of this problem is also illustrated on data related to the configuration of PML nuclear bodies in mammalian fibroblast cells

Uncertainty-aware estimation of population abundance using machine learning

Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classification based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality of the classification. Because collecting groundtruth is tedious, a method for estimating the potential errors in large datasets based on limited groundtruth is needed. We propose a method that improves classification quality by using limited groundtruth data to extrapolate the po-tential errors in larger datasets. It significantly improves the counting of elements per class. We further propose visualization designs for understanding and evaluating the classification un-certainty. They support end-users in considering the impact of potential misclassifications for interpreting the classification output. This work was developed to address the needs of ecologists studying fish population abundance using computer vision, but generalizes to a larger range of applications. Our method is largely applicable for a variety of Machine Learning technologies, and our visualizations further support their transfer to end-users

Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns

Abstract. The field of bioimage informatics concerns the development and use of methods for computational analysis of biological images. Traditionally, analysis of such images has been done manually. Manual annotation is, however, slow, expensive, and often highly variable from one expert to another. Furthermore, with modern automated microscopes, hundreds to thousands of images can be collected per hour, making manual analysis infeasible. This field borrows from the pattern recognition and computer vision literature (which contain many techniques for image processing and recognition), but has its own unique challenges and tradeoffs. Fluorescence microscopy images represent perhaps the largest class of biological images for which automation is needed. For this modality, typical problems include cell segmentation, classification of phenotypical response, or decisions regarding differentiated responses (treatment vs. control setting). This overview focuses on the problem of subcellular location determination as a running example, but the techniques discussed are often applicable to other problems.

Facultas toimintakyvyn arviointi -projektin vaikuttavuus

Toimintakyky on noussut viime vuosien aikana tärkeäksi tekijäksi ikääntyvän väestön hyvinvoinnin ja työurien pidentämisen näkökulmasta. Hyvä toimintakyky ja terveys ovat edellytys sille, että työelämässä jaksetaan pidempään. Toimintakyvyllä kuvataan ihmisen yleistä kykyä reagoida ympäristöönsä ja toimia siinä. Toiminta- ja työkykyyn on kohdistuttu useita hankkeita 1990- ja 2000-luvulla. Suomalainen Lääkäriseura Duodecim ja Työeläkevakuuttajat TELA toteuttivat vuosina 2006–2008 Facultas toimintakyvyn arviointi -projektin. Projekti toteutettiin laatimalla suositukset toimintakyvyn arvioinnista ja lääkäreille suunnatulla koulutuksella. Suositukset laadittiin neljästä sairausryhmästä, jotka ovat mielialahäiriöt, alaselän ja niskan sairaudet, suurten nivelten sairaudet ja krooninen kipu. Näiden sairausryhmien kohdalla arvioinnissa on koettu eniten ongelmia joko sairauden laadun tai määrän vuoksi. Lisäksi lääkärikunnalta on puuttunut yhtenäinen käytäntö toimintakyvyn arvioinnin toteuttamiseksi. Facultas-projektin tavoitteena oli yhdenmukaistaa toimintakyvyn arviointia lääkärikunnan parissa, parantaa hoitavien lääkäreiden toimintaedellytyksiä sekä yhtenäistää lääkärilausuntoja. Projektin tavoitteen mukainen lääkärinlausuntojen yhdenmukaistaminen tekee sosiaaliturvaetuuksien arviointiprosessin läpinäkyvämmäksi, tasapuolisemmaksi ja oikeudenmukaisemmaksi. Tämän pro gradu -tutkielman tavoitteena on tarkastella Facultas toimintakyvyn arviointi -projektin vaikuttavuutta vakuutuslääkäreiden ja hoitavien lääkäreiden näkökulmista. Tutkielman tavoitteena on selvittää, miten työeläkelaitoksiin saapuneet lääkärilausunnot ovat muuttuneet ja miten työkyvyttömyyden arviointiprosessi on muuttunut työeläkelaitoksissa Facultas-projektin myötä. Lisäksi selvitetään millaisia vaikutuksia projektilla on ollut lääkärikunnan käytännön työhön. Tutkimuskysymyksiin vastataan kymmenellä teemahaastattelulla. Tutkielma edustaa kvalitatiivista tapaustutkimusta. Tutkimustulosten mukaan Facultas toimintakyvyn arviointi -projekti onnistui monelta osin keskeisten tavoitteidensa saavuttamisessa. Työeläkelaitoksiin saapuneiden lääkärinlausuntojen taso on osittain parantunut ja tämän myötä projekti on vaikuttanut myös työkyvyttömyyden arviointiprosessiin työeläkelaitoksissa. Yhdenmukainen toimintakyvyn arviointi tekee arviointiprosessista helpompaa ja selkeämpää. Lisäksi hoitavat lääkärit ovat saaneet projektin myötä helppokäyttöisiä työvälineitä toimintakyvyn arviointiin. Koska lääkärinlausuntojen taso on parantunut, voidaan myös olettaa hakijan oikeusturvan ja työkyvyttömyyseläkkeen ratkaisuprosessin läpinäkyvyyden lisääntyneen. Haastatteluissa nousi esiin myös monia keinoja, joilla toimintakyvyn arviointia voitaisiin myös tulevaisuudessa parantaa. Toimintakyvyn arvioinnin kehittämisen toivottiin haastateltavien keskuudessa olevan pitkäaikainen prosessi. Asiasanat:Facultas toimintakyvyn arviointi -projekti, toimintakyky, työkyky, työkyvyttöm