A multiresolution approach to automated classification of protein subcellular location images

<p>Abstract</p> <p>Background</p> <p>Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem.</p> <p>Results</p> <p>We report here a novel approach for the classification of subcellular location patterns by classifying in multiresolution subspaces. Our system is able to work with any feature set and any classifier. It consists of multiresolution (MR) decomposition, followed by feature computation and classification in each MR subspace, yielding local decisions that are then combined into a global decision. With 26 texture features alone and a neural network classifier, we obtained an increase in accuracy on the 2D HeLa data set to 95.3%.</p> <p>Conclusion</p> <p>We demonstrate that the space-frequency localized information in the multiresolution subspaces adds significantly to the discriminative power of the system. Moreover, we show that a vastly reduced set of features is sufficient, consisting of our novel modified Haralick texture features. Our proposed system is general, allowing for any combinations of sets of features and any combination of classifiers.</p

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

Multi-domain learning CNN model for microscopy image classification

For any type of microscopy image, getting a deep learning model to work well requires considerable effort to select a suitable architecture and time to train it. As there is a wide range of microscopes and experimental setups, designing a single model that can apply to multiple imaging domains, instead of having multiple per-domain models, becomes more essential. This task is challenging and somehow overlooked in the literature. In this paper, we present a multi-domain learning architecture for the classification of microscopy images that differ significantly in types and contents. Unlike previous methods that are computationally intensive, we have developed a compact model, called Mobincep, by combining the simple but effective techniques of depth-wise separable convolution and the inception module. We also introduce a new optimization technique to regulate the latent feature space during training to improve the network's performance. We evaluated our model on three different public datasets and compared its performance in single-domain and multiple-domain learning modes. The proposed classifier surpasses state-of-the-art results and is robust for limited labeled data. Moreover, it helps to eliminate the burden of designing a new network when switching to new experiments

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

BACKGROUND: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images. RESULTS: We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5–15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images. CONCLUSIONS: The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction

Contributions to Statistical Image Analysis for High Content Screening.

Images of cells incubated with fluorescent small molecule probes can be used to infer where the compounds distribute within cells. Identifying the spatial pattern of compound localization within each cell is very important problem for which adequate statistical methods do not yet exist. First, we asked whether a classifier for subcellular localization categories can be developed based on a training set of manually classified cells. Due to challenges of the images such as uneven field illumination, low resolution, high noise, variation in intensity and contrast, and cell to cell variability in probe distributions, we constructed texture features for contrast quantiles conditioning on intensities, and classifying on artificial cells with same marginal distribution but different conditional distribution supported that this conditioning approach is beneficial to distinguish different localization distributions. Using these conditional features, we obtained satisfactory performance in image classification, and performed to dimension reduction and data visualization. As high content images are subject to several major forms of artifacts, we are interested in the implications of measurement errors and artifacts on our ability to draw scientifically meaningful conclusions from high content images. Specifically, we considered three forms of artifacts: saturation, blurring and additive noise. For each type of artifacts, we artificially introduced larger amount, and aimed to understand the bias by `Simulation Extrapolation' (SIMEX) method, applied to the measurement errors for pairwise centroid distances, the degree of eccentricity in the class-specific distributions, and the angles between the dominant axes of variability for different categories. Finally, we briefly considered the analysis of time-point images. Small molecule studies will be more focused. Specifically, we consider the evolving patterns of subcellular staining from the moment that a compound is introduced into the cell culture medium, to the point that steady state distribution is reached. We construct the degree to which the subcellular staining pattern is concentrated in or near the nucleus as the features of timecourse data set, and aim to determine whether different compounds accumulate in different regions at different times, as characterized in terms of their position in the cell relative to the nucleus.Ph.D.StatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91460/1/liufy_1.pd

Comparison of Artificial Intelligence based approaches to cell function prediction

Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels

Statistical Reconstruction Methods for 3D Imaging of Biological Samples with Electron Microscopy

Electron microscopy has emerged as the leading method for the in vivo study of biological structures such as cells, organelles, protein molecules and virus like particles. By providing 3D images up to near atomic resolution, it plays a significant role in analyzing complex organizations, understanding physiological functions and developing medicines. The 3D images representing the electrostatic potential distribution are reconstructed by utilizing the 2D projection images of the target acquired by electron microscope. There are two main 3D reconstruction techniques in the field of electron microscopy: electron tomography (ET) and single particle reconstruction (SPR). In ET, the projection images are acquired by rotating the specimen for different angles. In SPR, the projection images are obtained by analyzing the images of multiple objects representing the same structure. Then, the tomographic reconstruction methods are applied in both methods to obtain the 3D image through the 2D projections.Physical and mechanical limitations can prevent to acquire projection images that cover the projection angle space completely and uniformly. Incomplete and non-uniform sampling of the projection angles results in anisotropic resolution in the image plane and generates artifacts. Another problem is that the total applied dose of electrons is limited in order to prevent the radiation damage to the biological target. Therefore, limited number of projection images with low signal to noise ratio can be used in the reconstruction process. This affects the resolution of the reconstructed image significantly. This study presents statistical methods to overcome these major challenges to obtain precise and high resolution images in electron microscopy.Statistical image reconstruction methods have been successful in recovering a signal from imperfect measurements due to their capability of utilizing a priori information. First, we developed a sequential application of a statistical method for ET. Then we extended the method to support projection angles freely distributed in 3D space and applied the method in SPR. In both applications, we observed the strength of the method in projection gap filling, robustness against noise, and resolving the high resolution details in comparison with the conventional reconstruction methods. Afterwards, we improved the method in terms of computation time by incorporating multiresolution reconstruction. Furthermore, we developed an adaptive regularization method to minimize the parameters required to be set by the user. We also proposed the local adaptive Wiener filter for the class averaging step of SPR to improve the averaging accuracy.The qualitative and quantitative analysis of the reconstructions with phantom and experimental datasets has demonstrated that the proposed reconstruction methods outperform the conventional reconstruction methods. These statistical approaches provided better image accuracy and higher resolution compared with the conventional algebraic and transfer domain based reconstruction methods. The methods provided in this study contribute to enhance our understanding of cellular and molecular structures by providing 3D images of those with improved accuracy and resolution

Histopathological image analysis: a review,”

Abstract-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