5,302 research outputs found

    Machine learning approach for segmenting glands in colon histology images using local intensity and texture features

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    Colon Cancer is one of the most common types of cancer. The treatment is planned to depend on the grade or stage of cancer. One of the preconditions for grading of colon cancer is to segment the glandular structures of tissues. Manual segmentation method is very time-consuming, and it leads to life risk for the patients. The principal objective of this project is to assist the pathologist to accurate detection of colon cancer. In this paper, the authors have proposed an algorithm for an automatic segmentation of glands in colon histology using local intensity and texture features. Here the dataset images are cropped into patches with different window sizes and taken the intensity of those patches, and also calculated texture-based features. Random forest classifier has been used to classify this patch into different labels. A multilevel random forest technique in a hierarchical way is proposed. This solution is fast, accurate and it is very much applicable in a clinical setup

    A novel neural network approach to cDNA microarray image segmentation

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    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

    Semantic Object Parsing with Local-Global Long Short-Term Memory

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    Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical for achieving such fine-grained pixel-level recognition. Prior methods often leverage the contextual information through post-processing predicted confidence maps. In this work, we propose a novel deep Local-Global Long Short-Term Memory (LG-LSTM) architecture to seamlessly incorporate short-distance and long-distance spatial dependencies into the feature learning over all pixel positions. In each LG-LSTM layer, local guidance from neighboring positions and global guidance from the whole image are imposed on each position to better exploit complex local and global contextual information. Individual LSTMs for distinct spatial dimensions are also utilized to intrinsically capture various spatial layouts of semantic parts in the images, yielding distinct hidden and memory cells of each position for each dimension. In our parsing approach, several LG-LSTM layers are stacked and appended to the intermediate convolutional layers to directly enhance visual features, allowing network parameters to be learned in an end-to-end way. The long chains of sequential computation by stacked LG-LSTM layers also enable each pixel to sense a much larger region for inference benefiting from the memorization of previous dependencies in all positions along all dimensions. Comprehensive evaluations on three public datasets well demonstrate the significant superiority of our LG-LSTM over other state-of-the-art methods.Comment: 10 page

    Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization

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    With the introduction of spectral-domain optical coherence tomography (OCT), resulting in a significant increase in acquisition speed, the fast and accurate segmentation of 3-D OCT scans has become evermore important. This paper presents a novel probabilistic approach, that models the appearance of retinal layers as well as the global shape variations of layer boundaries. Given an OCT scan, the full posterior distribution over segmentations is approximately inferred using a variational method enabling efficient probabilistic inference in terms of computationally tractable model components: Segmenting a full 3-D volume takes around a minute. Accurate segmentations demonstrate the benefit of using global shape regularization: We segmented 35 fovea-centered 3-D volumes with an average unsigned error of 2.46 ±\pm 0.22 {\mu}m as well as 80 normal and 66 glaucomatous 2-D circular scans with errors of 2.92 ±\pm 0.53 {\mu}m and 4.09 ±\pm 0.98 {\mu}m respectively. Furthermore, we utilized the inferred posterior distribution to rate the quality of the segmentation, point out potentially erroneous regions and discriminate normal from pathological scans. No pre- or postprocessing was required and we used the same set of parameters for all data sets, underlining the robustness and out-of-the-box nature of our approach.Comment: Accepted for publication in Medical Image Analysis (MIA), Elsevie

    Automatic Segmentation of Cells of Different Types in Fluorescence Microscopy Images

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    Recognition of different cell compartments, types of cells, and their interactions is a critical aspect of quantitative cell biology. This provides a valuable insight for understanding cellular and subcellular interactions and mechanisms of biological processes, such as cancer cell dissemination, organ development and wound healing. Quantitative analysis of cell images is also the mainstay of numerous clinical diagnostic and grading procedures, for example in cancer, immunological, infectious, heart and lung disease. Computer automation of cellular biological samples quantification requires segmenting different cellular and sub-cellular structures in microscopy images. However, automating this problem has proven to be non-trivial, and requires solving multi-class image segmentation tasks that are challenging owing to the high similarity of objects from different classes and irregularly shaped structures. This thesis focuses on the development and application of probabilistic graphical models to multi-class cell segmentation. Graphical models can improve the segmentation accuracy by their ability to exploit prior knowledge and model inter-class dependencies. Directed acyclic graphs, such as trees have been widely used to model top-down statistical dependencies as a prior for improved image segmentation. However, using trees, a few inter-class constraints can be captured. To overcome this limitation, polytree graphical models are proposed in this thesis that capture label proximity relations more naturally compared to tree-based approaches. Polytrees can effectively impose the prior knowledge on the inclusion of different classes by capturing both same-level and across-level dependencies. A novel recursive mechanism based on two-pass message passing is developed to efficiently calculate closed form posteriors of graph nodes on polytrees. Furthermore, since an accurate and sufficiently large ground truth is not always available for training segmentation algorithms, a weakly supervised framework is developed to employ polytrees for multi-class segmentation that reduces the need for training with the aid of modeling the prior knowledge during segmentation. Generating a hierarchical graph for the superpixels in the image, labels of nodes are inferred through a novel efficient message-passing algorithm and the model parameters are optimized with Expectation Maximization (EM). Results of evaluation on the segmentation of simulated data and multiple publicly available fluorescence microscopy datasets indicate the outperformance of the proposed method compared to state-of-the-art. The proposed method has also been assessed in predicting the possible segmentation error and has been shown to outperform trees. This can pave the way to calculate uncertainty measures on the resulting segmentation and guide subsequent segmentation refinement, which can be useful in the development of an interactive segmentation framework

    A Survey on Deep Learning in Medical Image Analysis

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    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

    Open tools for dendrochronology. Advances in sample digitization and deep learning methods for image segmentation

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    Dendrochronological techniques are paramount in forest research. The current climate change scenario and the central role of forests in biogeophysical cycles enforce the importance of novel techniques to get accurate data from trees and their relationship with the environment in faster ways. Recent technological advances and the place of open source software and hardware are making free, user-developed tools for forest research available to the research community. The aim of this Ph.D. thesis is the development of tools for image acquisition and data collection in dendrochronology based on open source software and hardware. Thus, four different tools for dendrochronological research are presented in five different chapters. The first chapter focuses on the development of a do-it-yourself tool based on open source hardware for image acquisition and wood sample digitization at high resolution. We used open hardware equipment from Arduino and Python programming to develop CaptuRING and published the entire free open source tool as: "CaptuRING: A Do-It-Yourself tool for wood sample digitization" in Methods in Ecology and Evolution, 2022; 13:1185-1191. Furthermore, the original software was registered in the Registro General de Propiedad Intelectual (00/2022/737) of Ministerio de Cultura y Deporte (Spain). The second chapter presents "How to build and install your own CaptuRING". This contribution presents a series of videos with a step-by-step guide to promote the use of CaptuRING in the research community. The manuscript and related videos have been submitted for publication. The third chapter describes ρ-MtreeRing. This free and open-source software, which is written in R, analyzes X-ray films from dendrochronological samples to get microdensity values automatically segmented through a graphical user interface. The open source tool and manuscript are published as: "ρ-MtreeRing. A graphical user interface for X-ray microdensity analysis" in Forests. 2021; 12(10):1405. The fourth chapter describes the potential of deep learning methods to automatically segment xylem vessels. We trained three different convolutional neural networks to segment vessels in stained wood microsections using the Keras framework in Python. Our results demonstrate the potential of these techniques to automatically segment xylem vessels and overcome derived problems from image illumination, which hamper segmentation using classical image segmentation methods. The manuscript is published as "Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images" in: Neural Computing & Applications, 2020; 32:17927-17939. The fifth chapter develops an algorithm to delineate annual ring limits in stained wood microsections of a species with diffuse porous wood using convolutional neural networks. We used Python for image processing and the Keras framework for the algorithm training. The results show the ability of this techniques to obtain accurate tree ring segmentation for quantitative wood anatomy, reaching similar or even outperforming conventional manual delimitation in most of the evaluated cases. The results of this chapter will be presented in the manuscript "Deep Learning for ring bordering in stained cross-sectional images". This PhD Thesis presents four open source tools to get accurate information from wood features to unveil how trees respond to the environment. From digitization at macroscopic perspective, automatic data collection and the development of feature segmentation on microscopic samples. The presented four novel dendrochronological tools based on open source software facilitates forest research in the current climate change scenario.Las técnicas dendrocronológicas son fundamentales en la investigación forestal. El escenario actual de cambio climático y el papel central de los bosques en los ciclos biogeofísicos subrayan la necesidad de nuevas técnicas para obtener de un modo ágil datos precisos de los árboles y de su relación con el medio ambiente. Los recientes avances tecnológicos, además de la disponibilidad actual del software y el hardware de código abierto están poniendo a disposición de la comunidad investigadora herramientas gratuitas desarrolladas por los usuarios para la investigación forestal. El objetivo de esta tesis doctoral es el desarrollo de herramientas para la adquisición de imágenes y la recogida de datos basadas en software y hardware de código abierto para el estudio dendrocronológico. Esta tesis presenta cuatro herramientas diferentes para esta rama científica en cinco capítulos diferentes. El primer capítulo se centra en el desarrollo de una herramienta "hágalo usted mismo" basada en hardware de código abierto para la adquisición de imágenes y la digitalización de muestras de madera a alta resolución. Usamos equipos de hardware abierto de Arduino y programación de Python para desarrollar CaptuRING y publicamos la herramienta completa de código abierto como: "CaptuRING: A Do-It-Yourself tool for wood sample digitization" en Methods in Ecology and Evolution, 2022; 13:1185-1191. Además, el software original fue registrado en el Registro General de Propiedad Intelectual (00/2022/737) del Ministerio de Cultura y Deporte (España). El segundo capítulo presenta "Cómo construir e instalar su propio CaptuRING" ("How to build and install your own CaptuRING"). Esta contribución presenta una serie de vídeos con una guía paso a paso para promover el uso de CaptuRING en la comunidad investigadora. El manuscrito y los vídeos relacionados se han enviado para su publicación. El tercer capítulo describe ρ-MtreeRing. Este software gratuito y de código abierto, que está escrito en R, analiza imágenes de rayos X de muestras dendrocronológicas para obtener valores de microdensidad automáticamente segmentados a través de una sencilla interfaz gráfica de usuario. La herramienta de código abierto y el manuscrito se publicaron como: "ρ-MtreeRing. A graphical user interface for X-ray microdensity analysis" en Forests. 2021; 12(10):1405. El cuarto capítulo describe el potencial de los métodos de aprendizaje profundo para segmentar automáticamente los vasos del xilema. Entrenamos tres redes neuronales convolucionales diferentes para segmentar vasos en cortes histológicos de madera utilizando el marco Keras en Python. Nuestros resultados demuestran el potencial de estas técnicas para segmentar automáticamente los vasos del xilema y superar los problemas derivados de la iluminación de la imagen, que dificultan la labor de métodos clásicos de segmentación de imágenes. El manuscrito se publicó como "Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images" en: Neural Computing & Applications. 2020; 32:17927-17939. El quinto capítulo desarrolla un algoritmo para delinear los límites anuales de los anillos en cortes histológicos de una especie con madera difuso-porosa utilizando redes neuronales convolucionales. Se utilizó Python para el procesamiento de imágenes y el marco Keras para el entrenamiento del algoritmo. Los resultados muestran la capacidad de estas técnicas para obtener una segmentación precisa de los anillos de los árboles para la anatomía cuantitativa de la madera alcanzando, en la mayoría de los casos evaluados, un rendimiento similar o incluso superior a la delimitación manual convencional. Los resultados de este capítulo se presentarán en el manuscrito "Deep Learning for ring bordering in stained cross-sectional images". Esta Tesis Doctoral presenta cuatro herramientas de código abierto para obtener información precisa de las características de la madera investigar cómo los árboles responden al entorno facilitando la investigación en el actual escenario de cambio climático.Escuela de DoctoradoDoctorado en Conservación y Uso Sostenible de Sistemas Forestale

    Fast global interactive volume segmentation with regional supervoxel descriptors

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    In this paper we propose a novel approach towards fast multi-class volume segmentation that exploits supervoxels in order to reduce complexity, time and memory requirements. Current methods for biomedical image segmentation typically require either complex mathematical models with slow convergence, or expensive-to-calculate image features, which makes them non-feasible for large volumes with many objects (tens to hundreds) of different classes, as is typical in modern medical and biological datasets. Recently, graphical models such as Markov Random Fields (MRF) or Conditional Random Fields (CRF) are having a huge impact in different computer vision areas (e.g. image parsing, object detection, object recognition) as they provide global regularization for multiclass problems over an energy minimization framework. These models have yet to find impact in biomedical imaging due to complexities in training and slow inference in 3D images due to the very large number of voxels. Here, we define an interactive segmentation approach over a supervoxel space by first defining novel, robust and fast regional descriptors for supervoxels. Then, a hierarchical segmentation approach is adopted by training Contextual Extremely Random Forests in a user-defined label hierarchy where the classification output of the previous layer is used as additional features to train a new classifier to refine more detailed label information. This hierarchical model yields final class likelihoods for supervoxels which are finally refined by a MRF model for 3D segmentation. Results demonstrate the effectiveness on a challenging cryo-soft X-ray tomography dataset by segmenting cell areas with only a few user scribbles as the input for our algorithm. Further results demonstrate the effectiveness of our method to fully extract different organelles from the cell volume with another few seconds of user interaction. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only
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