Biology subjects

Oberai, A. A.

Publications and source records attributed to Oberai, A. A..

5 recordsLinked to original sources

bioRxiv2025

Unsupervised Detection of Rare Events in Liquid Biopsy Assays

The use of liquid biopsies in the detection, diagnosis and treatment monitoring of different types of cancers and other diseases often requires identifying and enumerating instances of analytes that are rare. Most current techniques that aim to computationally isolate these rare instances or events first learn the signature of the event, and then scan the appropriate biological assay for this signature. While such techniques have proven to be very useful, they are limited because they must first establish what signature to look for, and only then identify events that are consistent with this signature. In contrast to this, in this study, we present an automated approach that does not require the knowledge of the signature of the rare event. It works by breaking the assay into a sequence of components, learning the probability distribution of these components, and then isolating those that are rare. This is done with the help of deep generative algorithms in an unsupervised manner, meaning without a-priori knowledge of the rare event associated with an analyte. In this study, this approach is applied to immunofluorescence microscopy images of peripheral blood, where it is shown that it successfully isolates biologically relevant events in blood from normal donors spiked with cancer-related cells and in blood from patients with late-stage breast cancer.

bioinformatics↗

bioRxiv2024

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Techniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. To train and test the cGAN, we constructed 30,000 elasticity and phase difference image pairs, where elasticity images were generated using a parametric model to simulate artificial samples, and phase difference images were computed using finite element analysis to simulate compression applied to the artificial samples. By applying both the cGAN and algebraic methods to simulated phase difference images, our results indicate the cGAN elasticity images exhibit better spatial resolution and sensitivity. We also evaluated the cGAN on experimental phase difference images of real spheroids embedded in hydrogels and compared the cGAN elasticity with the algebraic elasticity, OCM, and confocal fluorescence microscopy, and found the cGAN elasticity is often more robust to noise, especially within stiff nuclei.

bioengineering↗

bioRxiv2023

Conditional Generative Learning for Medical Image Imputation

Image imputation refers to the task of generating a type of medical image given images of another type. This task becomes challenging when the difference between the available images, and the image to be imputed is large. In this manuscript, one such application, derived from the dynamic contrast enhanced computed tomography (CECT) imaging of the kidneys, is considered: given an incomplete sequence of three CECT images, we are required to the impute the missing image. This task is posed as one of probabilistic inference and a generative algorithm to generate samples of the imputed image, conditioned on the available images, is developed, trained, and tested. The output of this algorithm is the "best guess" of the imputed image, and a pixel-wise image of variance in the imputation. It is demonstrated that this best guess is more accurate than those generated by other, deterministic deep-learning based algorithms, including ones which utilize additional information and more complex loss terms. It is also shown the pixel-wise variance image, which quantifies the confidence in the reconstruction, can be used to determine whether the result of the imputation meets a specified accuracy threshold and is therefore appropriate for a downstream task.

cancer biology↗

bioRxiv2022

Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks

Brain extraction, which refers to the task of segmenting brain tissue in an MR image of a subject, forms an essential first step for many quantitative neuroimaging applications. These include quantifying grey and white matter volumes, monitoring neurological diseases like multiple sclerosis (MS) and Alzheimers disease, and estimating brain atrophy. Over the years several algorithms that automate brain extraction have been proposed. More recently, novel image-to-image deep learning methods have been implemented for this task, and have demonstrated significant gains in accuracy and robustness. However, to our knowledge, none of these algorithms account for the uncertainty that is inherent in brain extraction. Motivated by this, we propose a novel, probabilistic deep learning algorithm for brain extraction that recasts this task as a Bayesian inference problem, and then utilizes a conditional generative adversarial network (cGAN) to solve it. The input to the generator network is an MR image of the head and the output is a collection of images of the brain that are drawn from a probability density conditioned on the input image. These images are used to generate a pixel-wise mean image, which serves as the best guess for an image of the brain, and a pixel-wise standard deviation image, which quantifies the uncertainty in the prediction. We test this algorithm on head MR images of fifty subjects and demonstrate that it is more accurate than a commonly used brain extraction tool, and that its performance compares well with the current state of the art in deep learning algorithms. We also demonstrate the utility of the estimates of uncertainty generated by the algorithm.

bioengineering↗

bioRxiv2019

Three-dimensional traction microscopy accounting for cell-induced matrix degradation

Tractions exerted by cells on the extracellular matrix (ECM) are critical in many important physiological and pathological processes such as embryonic morpho-genesis, wound healing, and cancer metastasis. Three-dimensional Traction Microscopy (3DTM) is a tool to quantify cellular tractions by first measuring the displacement field in the ECM in response to these tractions, and then using this measurement to infer tractions. Most applications of 3DTM have assumed that the ECM has spatially-uniform mechanical properties, but cells secrete enzymes that can locally degrade the ECM. In this work, a novel computational method is developed to quantify both cellular tractions and ECM degradation. In particular, the ECM is modeled as a hyperelastic, Neo-Hookean solid, whose material parameters are corrupted by a single degradation parameter. The feasibility of determining both the traction and the degradation parameter is first demonstrated by showing the existence and uniqueness of the solution. An inverse problem is then formulated to determine the nodal values of the traction vector and the degradation parameter, with the objective of minimizing the difference between a predicted and measured displacement field, under the constraint that the predicted displacement field satisfies the equation of equilibrium. The inverse problem is solved by means of a gradient-based optimization approach, and the gradient is computed efficiently using appropriately derived adjoint fields. The computational method is validated in-silico using a geometrically accurate neuronal cell model and synthetic traction and degradation fields. It is found that the method accurately recovers both the traction and degradation fields. Moreover, it is found that neglecting ECM degradation can yield significant errors in traction measurements. Our method can extend the range of applicability of 3DTM.

biophysics↗