Biology subjects

Kolachalama, V. B.

Publications and source records attributed to Kolachalama, V. B..

8 recordsLinked to original sources

bioRxiv2024

FourierMIL: Fourier filtering-based multiple instance learning for whole slide image analysis

Recent advancements in computer vision, driven by convolutional neural network, multilayer perceptron and transformer architectures, have significantly improved the analysis on natural images. Despite their potential, the application of these architectures in digital pathology, specifically for analyzing gigapixel-resolution whole-slide images (WSIs), remains challenging due to the extensive and variable sizes of these images. Here we present a multiple instance learning framework that leverages the discrete Fourier transform and learns from WSIs. Dubbed as FourierMIL, our framework is designed to capture both global and local dependencies within WSIs. To validate the efficacy of our model, we conducted extensive experiments on a prevalent computational pathology challenge: tumor classification. Our results demonstrate that FourierMIL outperforms existing state-of-the-art methods, marking a significant advancement in the field of digital pathology and highlighting the potential of attention-free architectures in managing the complexities related to WSI analysis. The code will be released for public access upon the manuscripts acceptance.

bioinformatics↗

bioRxiv2024

Digital pathology assessment of kidney glomerular filtration barrier ultrastructure in an animal model of podocytopathy

BackgroundTransmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability. MethodsWe developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs. ResultsMean (95% confidence interval) GBM segmentation accuracy, calculated as Jaccard index, was 0.73 (0.70-0.76) for WT and 0.85 (0.83-0.87) for ILK cKO TEM images. Automated and manual GBM width measurements were similar for both WT (p=0.49) and ILK cKO (p=0.06) specimens. While automated and manual PFP width measurements were similar for WT (p=0.89), they differed for ILK cKO (p<0.05) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM (p<0.05) and PFP (p<0.05) width measurements. This phenotypic difference was reflected in the automated GBM (p<0.05) more than PFP (p=0.06) widths. ConclusionsThese results suggest that certain automated measurements enabled via deep learning-based digital pathology tools could distinguish healthy kidneys from those with podocytopathy. Our proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research and translate into clinical diagnosis. Key pointsO_LIWe leveraged U-Net architecture in an algorithm to measure the widths of glomerular basement membrane and podocyte foot processes. C_LIO_LIDeep learning-based automated measurement of glomerular filtration barrier morphology has promise in podocytopathy research and diagnosis. C_LI

pathology↗

bioRxiv2023

Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival

Multimodal machine learning models are being developed to analyze pathology images and other modalities, such as gene expression, to gain clinical and biological in-sights. However, most frameworks for multimodal data fusion do not fully account for the interactions between different modalities. Here, we present an attention-based fusion architecture that integrates a graph representation of pathology images with gene expression data and concomitantly learns from the fused information to predict patient-specific survival. In our approach, pathology images are represented as undirected graphs, and their embeddings are combined with embeddings of gene expression signatures using an attention mechanism to stratify tumors by patient survival. We show that our framework improves the survival prediction of human non-small cell lung cancers, out-performing existing state-of-the-art approaches that lever-age multimodal data. Our framework can facilitate spatial molecular profiling to identify tumor heterogeneity using pathology images and gene expression data, complementing results obtained from more expensive spatial transcriptomic and proteomic technologies.

bioinformatics↗

bioRxiv2023

Graph perceiver network for lung tumor and premalignant lesion stratification from histopathology

Bronchial premalignant lesions (PMLs) precede the development of invasive lung squamous carcinoma (LUSC), posing a significant challenge in distinguishing those likely to advance to LUSC from those that might regress without intervention. In this context, we present a novel computational approach, the Graph Perceiver Network (GRAPE-Net), leveraging hematoxylin and eosin (H&E) stained whole slide images (WSIs) to stratify endobronchial biopsies of PMLs across a spectrum from normal to tumor lung tissues. GRAPE-Net outperforms existing frameworks in classification accuracy predicting LUSC, lung adenocarcinoma (LUAD), and non-tumor (normal) lung tissue on The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets containing lung resection tissues while efficiently generating pathologist-aligned, class-specific heatmaps. The network was further tested using endobronchial biopsies from two data cohorts, containing normal to carcinoma in situ histology, and it demonstrated a unique capability to differentiate carcinoma in situ lung squamous PMLs based on their progression status to invasive carcinoma. The network may have utility in stratifying PMLs for chemoprevention trials or more aggressive follow-up.

pathology↗

bioRxiv2019

Development and validation of a deep learning framework for Alzheimers disease classification

Alzheimers disease (AD) is the primary cause of dementia worldwide (1), with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and magnetic resonance imaging (MRI) to identify likely cases, yet effective practices remain variably-applied and lacking in sensitivity and specificity (2). Here we report an explainable deep learning strategy that delineates unique AD signatures from multimodal inputs of MRI, age, gender, and mini-mental state examination (MMSE) score. Our framework linked a fully convolutional network (FCN) to a multilayer perceptron (MLP) to construct high resolution maps of disease probability from local brain structure. This enabled precise, intuitive visualization of individual AD risk en route to accurate diagnosis. The model was trained using clinically-diagnosed AD and cognitively normal (NC) subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset (n=417) (3), and validated on three independent cohorts: the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n=382) (4), the Framingham Heart Study (FHS, n=102) (5), and the National Alzheimers Coordinating Center (NACC, n=582) (6). Model performance was consistent across datasets, with mean accuracy values of 0.966, 0.948, 0.815, and 0.916 for ADNI, AIBL, FHS and NACC, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n=11), and high-risk cerebral regions predicted by the model closely tracked postmortem histopathological findings. This framework provides a clinically-adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for AD diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

neuroscience↗

bioRxiv2019

Sustained Ca2+ mobilizations: a quantitative approach to predict their importance in cell-cell communication and wound healing

Epithelial wound healing requires the coordination of cells to migrate as a unit over the basement membrane after injury. To understand the process of this coordinated movement, it is critical to study the dynamics of cell-cell communication. We developed a method to characterize the injury-induced sustained Ca2+ mobilizations that travel between cells for periods of time up to several hours. These events of communication are concentrated along the wound edge and are reduced in cells further away from the wound. Our goal was to delineate the role and contribution of these sustained mobilizations and using MATLAB analyses, we determined the probability of cell-cell communication events in in vitro models and ex vivo organ culture models. We demonstrated that the injury response was complex and represented the activation of a number of receptors. In addition, we found that pannexin channels mediated the cell-cell communication and motility. Furthermore, the sustained Ca2+ mobilizations are associated with changes in cell morphology and motility during wound healing. The results demonstrate that both purinoreceptors and pannexins regulate the sustained Ca2+ mobilization necessary for cell-cell communication in wound healing.

cell biology↗

bioRxiv2018

Predicting bilateral knee pain from MR imaging using deep neural networks

Background and objectiveIt remains difficult to characterize pain in knee joints with osteoarthritis solely by radiographic findings. We sought to understand how advanced machine learning methods such as deep neural networks can be used to analyze raw MRI scans and predict bilateral knee pain, independent of other risk factors. MethodsWe developed a deep learning framework to associate information from MRI slices taken from the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain. Model training was performed by first extracting features from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices. The extracted features from all the 2D slices were subsequently combined to directly associate using a fused deep neural network with the output of interest as a binary classification problem. ResultsThe deep learning model resulted in predicting bilateral knee pain on test data with 70.1% mean accuracy, 51.3% mean sensitivity, and 81.6% mean specificity. Systematic analysis of the predictions on the test data revealed that the model performance was consistent across subjects of different Kellgren-Lawrence grades. ConclusionThe study demonstrates a proof of principle that a machine learning approach can be applied to associate MR images with bilateral knee pain. SIGNIFICANCE AND INNOVATIONKnee pain is typically considered as an early indicator of osteoarthritis (OA) risk. Emerging evidence suggests that MRI changes are linked to pre-clinical OA, thus underscoring the need for building image-based models to predict knee pain. We leveraged a state-of-the-art machine learning approach to associate raw MR images with bilateral knee pain, independent of other risk factors.

bioengineering↗

bioRxiv2018

Segmentation of Glomeruli Within Trichrome Images Using Deep Learning

IntroductionThe number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute as standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive and non-standardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies. MethodsTrichrome-stained images (n=275) from renal biopsies of 171 chronic kidney disease patients treated at the Boston Medical Center from 2009-12 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by three experts into three categories: (a) No glomerulus, (b) Normal or partially sclerosed glomerulus and (c) Globally sclerosed glomerulus. This led to identification of 751 unique images representing non{-}glomerular regions, 611 images with either normal or partially sclerosed (NPS) glomeruli and 134 images with globally sclerosed (GS) glomeruli. A convolutional neural network (CNN) was trained with cropped images as inputs and corresponding labels as output. Using this model, an image processing routine was developed to scan the test data images to segment the GS glomeruli. ResultsThe CNN model was able to accurately discriminate non-glomerular images from NPS and GS images (Performance on test data - Accuracy: 92.67{+/-}2.02% and Kappa: 0.8681{+/-}0.0392). The segmentation model that was based on the CNN multi-label classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628). ConclusionThis work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.

pathology↗