3,714 research outputs found
Current Approaches for Image Fusion of Histological Data with Computed Tomography and Magnetic Resonance Imaging
Classical analysis of biological samples requires the destruction of the tissue’s integrity by cutting or grinding it down to thin slices for (Immuno)-histochemical staining and microscopic analysis. Despite high specificity, encoded in the stained 2D section of the whole tissue, the structural information, especially 3D information, is limited. Computed tomography (CT) or magnetic resonance imaging (MRI) scans performed prior to sectioning in combination with image registration algorithms provide an opportunity to regain access to morphological characteristics as well as to relate histological findings to the 3D structure of the local tissue environment. This review provides a summary of prevalent literature addressing the problem of multimodal coregistration of hard- and soft-tissue in microscopy and tomography. Grouped according to the complexity of the dimensions, including image-to-volume (2D ⟶ 3D), image-to-image (2D ⟶ 2D), and volume-to-volume (3D ⟶ 3D), selected currently applied approaches are investigated by comparing the method accuracy with respect to the limiting resolution of the tomography. Correlation of multimodal imaging could position itself as a useful tool allowing for precise histological diagnostic and allow the a priori planning of tissue extraction like biopsies
Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction
Survival prediction is a complicated ordinal regression task that aims to
predict the ranking risk of death, which generally benefits from the
integration of histology and genomic data. Despite the progress in joint
learning from pathology and genomics, existing methods still suffer from
challenging issues: 1) Due to the large size of pathological images, it is
difficult to effectively represent the gigapixel whole slide images (WSIs). 2)
Interactions within tumor microenvironment (TME) in histology are essential for
survival analysis. Although current approaches attempt to model these
interactions via co-attention between histology and genomic data, they focus on
only dense local similarity across modalities, which fails to capture global
consistency between potential structures, i.e. TME-related interactions of
histology and co-expression of genomic data. To address these challenges, we
propose a Multimodal Optimal Transport-based Co-Attention Transformer framework
with global structure consistency, in which optimal transport (OT) is applied
to match patches of a WSI and genes embeddings for selecting informative
patches to represent the gigapixel WSI. More importantly, OT-based co-attention
provides a global awareness to effectively capture structural interactions
within TME for survival prediction. To overcome high computational complexity
of OT, we propose a robust and efficient implementation over micro-batch of WSI
patches by approximating the original OT with unbalanced mini-batch OT.
Extensive experiments show the superiority of our method on five benchmark
datasets compared to the state-of-the-art methods. The code is released.Comment: 11 pages, 4 figures, accepted by ICCV 202
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
Recommended from our members
Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks
Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment. In this paper, three grey difference statistics-based features, namely three Gray-Level Co-occurrence Matrix (GLCM) based features and fifteen Gray Gradient Co-occurrence Matrix (GGCM) features were calculated by correlative analysis. Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance. A cross-entropy is then calculated to evaluate the proposed Sigmoid input and the ten-hidden layer network. The results depicted that SCG-BPNN with texture features performs high recognition rate compared to using morphological features, such as shape, size, contour, thickness and other geometry-based features for the classification. The proposed method also has a high accuracy rate of 87.2% compared to the Back-Propagation Neural Network (BPNN), Back-Propagation Hopfield Neural Network (BPHNN) and Convolutional Neural Network (CNN)
Artificial Intelligence for Digital and Computational Pathology
Advances in digitizing tissue slides and the fast-paced progress in
artificial intelligence, including deep learning, have boosted the field of
computational pathology. This field holds tremendous potential to automate
clinical diagnosis, predict patient prognosis and response to therapy, and
discover new morphological biomarkers from tissue images. Some of these
artificial intelligence-based systems are now getting approved to assist
clinical diagnosis; however, technical barriers remain for their widespread
clinical adoption and integration as a research tool. This Review consolidates
recent methodological advances in computational pathology for predicting
clinical end points in whole-slide images and highlights how these developments
enable the automation of clinical practice and the discovery of new biomarkers.
We then provide future perspectives as the field expands into a broader range
of clinical and research tasks with increasingly diverse modalities of clinical
data
Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Prediction
Multimodal learning significantly benefits cancer survival prediction,
especially the integration of pathological images and genomic data. Despite
advantages of multimodal learning for cancer survival prediction, massive
redundancy in multimodal data prevents it from extracting discriminative and
compact information: (1) An extensive amount of intra-modal task-unrelated
information blurs discriminability, especially for gigapixel whole slide images
(WSIs) with many patches in pathology and thousands of pathways in genomic
data, leading to an ``intra-modal redundancy" issue. (2) Duplicated information
among modalities dominates the representation of multimodal data, which makes
modality-specific information prone to being ignored, resulting in an
``inter-modal redundancy" issue. To address these, we propose a new framework,
Prototypical Information Bottlenecking and Disentangling (PIBD), consisting of
Prototypical Information Bottleneck (PIB) module for intra-modal redundancy and
Prototypical Information Disentanglement (PID) module for inter-modal
redundancy. Specifically, a variant of information bottleneck, PIB, is proposed
to model prototypes approximating a bunch of instances for different risk
levels, which can be used for selection of discriminative instances within
modality. PID module decouples entangled multimodal data into compact distinct
components: modality-common and modality-specific knowledge, under the guidance
of the joint prototypical distribution. Extensive experiments on five cancer
benchmark datasets demonstrated our superiority over other methods
Multimodal information spaces for content-based image retrieval
Abstract. Image collections today are increasingly larger in size, and they continue to grow constantly. Without the help of image search systems these abundant visual records collected in many different fields and domains may remain unused and inaccessible. Many available image databases often contain complementary modalities, such as attached text resources, which can be used to build an index for querying with keywords. However, sometimes users do not have or do not know the right words to express what they need, and, in addition, keywords do not express all the visual variations that an image may contain. Using example images as queries can be viewed as an alternative in different scenarios such as searching images using a mobile phone with a coupled camera, or supporting medical diagnosis by searching a large medical image collection. Still, matching only visual features between the query and image databases may lead to undesirable results from the user's perspective. These conditions make the process of finding relevant images for a specific information need very challenging, time consuming or even frustrating. Instead of considering only a single data modality to build image search indexes, the simultaneous use of both, visual and text data modalities, has been suggested. Non-visual information modalities may provide complementary information to enrich the image representation. The goal of this research work is to study the relationships between visual contents and text terms to build useful indexes for image search. A family of algorithms based on matrix factorization are proposed for extracting the multimodal aspects from an image collection. Using this knowledge about how visual features and text terms correlate, a search index is constructed, which can be searched using keywords, example images or combinations of both. Systematic experiments were conducted on different data sets to evaluate the proposed indexing algorithms. The experimental results showed that multimodal indexing is an effective strategy for designing image search systems.Las colecciones de imágenes hoy en dÃa son muy grandes y crecen constantemente. Sin la ayuda de sistemas para la búsqueda de imágenes esos abundantes registros visuales que han sido recolectados en diferentes areas del conocimiento pueden permanecer aislados sin uso. Muchas bases de datos de imágenes contienen modalidades de datos complementarias, como los recursos textuales que pueden ser utilizados para crear Ãndices de búsqueda. Sin embargo, algunas veces los usuarios no tienen o no saben qué palabras utilizar para encontrar lo que necesitan, y adicionalmente, las palabras clave no expresan todas las variaciones visuales que una imagen puede tener. Utilizar imágenes de ejemplo para expresar la consulta puede ser visto como una alternativa, por ejemplo buscar imágenes con teléfonos móviles, o dar soporte al diagnóstico médico con las imágenes de los pacientes. Aún asÃ, emparejar correctamente las caracterÃsticas visuales de la consulta y las imágenes en la base de datos puede llevar a resultados semánticamente incorrectos. Estas condiciones hacen que el proceso de buscar imágenes relevantes para una necesidad de información particular sea una tarea difÃcil, que consume mucho tiempo o que incluso puede ser frustrante. En lugar de considerar solo una modalidad de datos para construir Ãndices de búsqueda para imágenes, el uso simultáneo de las modalidades visual y textual ha sido sugerido. Las modalidades no visuales pueden proporcionar información complementaria para enriquecer la representación de las imágenes. El objetivo de este trabajo de investigación es estudiar las relaciones entre los contenidos visuales y los términos textuales, para construir Ãndices de búsqueda útiles. Este trabajo propone una familia de algoritmos basados en factorización de matrices para extraer los aspectos multimodales de una colección de imágenes. Utilizando este conocimiento acerca de cómo las caracterÃsticas visuales se correlacionan con los términos textuales, se construye un Ãndice que puede ser consultado con palabras clave, imágenes de ejemplo o por combinaciones de estas dos. Se realizaron experimentos sistemáticos en diferentes conjuntos de datos para evaluar los algoritmos de indexamiento propuestos. Los resultados muestran que el indexamiento multimodal es una estrategia efectiva para diseñar sistemas de búsqueda de imágenes.Doctorad
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
- …