Significance: Our study introduces an application of deep learning to virtually generate fluorescence images to reduce the burdens of cost and time from considerable effort in sample preparation related to chemical fixation and staining.
Aim: The objective of our work was to determine how successfully deep learning methods perform on fluorescence prediction that depends on structural and/or a functional relationship between input labels and output labels.
Approach: We present a virtual-fluorescence-staining method based on deep neural networks (VirFluoNet) to transform co-registered images of cells into subcellular compartment-specific molecular fluorescence labels in the same field-of-view. An algorithm based on conditional generative adversarial networks was developed and trained on microscopy datasets from breast-cancer and bone-osteosarcoma cell lines: MDA-MB-231 and U2OS, respectively. Several established performance metrics—the mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and structural-similarity-index (SSIM)—as well as a novel performance metric, the tolerance level, were measured and compared for the same algorithm and input data.
Results: For the MDA-MB-231 cells, F-actin signal performed the fluorescent antibody staining of vinculin prediction better than phase-contrast as an input. For the U2OS cells, satisfactory metrics of performance were archieved in comparison with ground truth. MAE is <0.005, 0.017, 0.012; PSNR is >40 / 34 / 33 dB; and SSIM is >0.925 / 0.926 / 0.925 for 4′,6-diamidino-2-phenylindole/hoechst, endoplasmic reticulum, and mitochondria prediction, respectively, from channels of nucleoli and cytoplasmic RNA, Golgi plasma membrane, and F-actin.
Conclusions: These findings contribute to the understanding of the utility and limitations of deep learning image-regression to predict fluorescence microscopy datasets of biological cells. We infer that predicted image labels must have either a structural and/or a functional relationship to input labels. Furthermore, the approach introduced here holds promise for modeling the internal spatial relationships between organelles and biomolecules within living cells, leading to detection and quantification of alterations from a standard training dataset.
Significance: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells’ morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies.
Aim: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies.
Approach: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning.
Results: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line.
Conclusions: The proposed epithelial–mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.
Diffusion tensor magnetic resonance imaging (DT-MRI) is increasingly used in clinical research and
applications for its ability to depict white matter tracts and for its sensitivity to microstructural and
architectural features of brain tissue. However, artifacts are common in clinical DT-MRI acquisitions.
Signal perturbations produced by such artifacts can be severe and neglecting to account for their
contribution can result in erroneous diffusion tensor values. The Robust Estimation of Tensors by Outlier
Rejection (RESTORE) has been demonstrated to be an effective method for improving tensor estimation
on a voxel-by-voxel basis in the presence of artifactual data points in diffusion weighted images. Despite
the very good performance of the RESTORE algorithm, there are some limitations and opportunities for
improvement. Instabilities in tensor estimation using RESTORE have been observed in clinical human
brain data. Those instabilities can come from the intrinsic high frequency spin inflow effects in non-DWIs
or from excluding too many data points from the fitting. This paper proposes several practical constraints
to the original RESTORE method. Results from Monte Carlo simulation indicate that the improved RESTORE method reduces the instabilities in tensor estimation observed from the original RESTORE method.
The longitudinal relaxation time, T1, can be estimated from two or more spoiled gradient recalled echo x
(SPGR) images with two or more flip angles and one or more repetition times (TRs). The function relating
signal intensity and the parameters are nonlinear; T1 maps can be computed from SPGR signals using
nonlinear least squares regression. A widely-used linear method transforms the nonlinear model by
assuming a fixed TR in SPGR images. This constraint is not desirable since multiple TRs are a clinically
practical way to reduce the total acquisition time, to satisfy the required resolution, and/or to combine
SPGR data acquired at different times. A new linear least squares method is proposed using the first order
Taylor expansion. Monte Carlo simulations of SPGR experiments are used to evaluate the accuracy and
precision of the estimated T1 from the proposed linear and the nonlinear methods. We show that the new
linear least squares method provides T1 estimates comparable in both precision and accuracy to those from
the nonlinear method, allowing multiple TRs and reducing computation time significantly.
Proc. SPIE. 5747, Medical Imaging 2005: Image Processing
KEYWORDS: Signal to noise ratio, Magnetic resonance imaging, Image segmentation, Error analysis, Diffusion, Magnetism, Interference (communication), Data acquisition, Monte Carlo methods, Data analysis
Signal intensity in magnetic resonance images (MRIs) is affected by random noise. Assessing noise-induced signal variance is important for controlling image quality. Knowledge of signal variance is required for correctly computing the chi-square value, a measure of goodness of fit, when fitting signal data to estimate quantitative parameters such as T1 and T2 relaxation times or diffusion tensor elements. Signal variance can be estimated from measurements of the noise variance in an object- and ghost-free region of the image background. However, identifying a large homogeneous region automatically is problematic. In this paper, a novel, fully automated approach for estimating the noise-induced signal variance in magnitude-reconstructed MRIs is proposed. This approach is based on the histogram analysis of the image signal intensity, explicitly by extracting the peak of the underlining Rayleigh distribution that would characterize the distribution of the background noise. The peak is extracted using a nonparametric univariate density estimation like the Parzen window density estimation; the corresponding peak position is shown here to be the expected signal variance in the object. The proposed method does not depend on prior foreground segmentation, and only one image with a small amount of background is required when the signal-to-noise ratio (SNR) is greater than three. This method is applicable to magnitude-reconstructed MRIs, though diffusion tensor (DT)-MRI is used here to demonstrate the approach.