Biology subjects

Lutnick, B. R.

Publications and source records attributed to Lutnick, B. R..

3 recordsLinked to original sources

bioRxiv2021

Generative modeling of histology tissue reduces human annotation effort for segmentation model development.

Segmentation of histology tissue whole side images is an important step for tissue analysis. Given enough annotated training data modern neural networks are capable accurate reproducible segmentation, however, the annotation of training datasets is time consuming. Techniques such as human in the loop annotation attempt to reduce this annotation burden, but still require a large amount of initial annotation. Semi-supervised learning, a technique which leverages both labeled and unlabeled data to learn features has shown promise for easing the burden of annotation. Towards this goal, we employ a recently published semi-supervised method: datasetGAN for the segmentation of glomeruli from renal biopsy images. We compare the performance of models trained using datasetGAN and traditional annotation and show that datasetGAN significantly reduces the amount of annotation required to develop a highly performing segmentation model. We also explore the usefulness of using datasetGAN for transfer learning and find that this greatly enhances the performance when a limited number of whole slide images are used for training.

pathology↗

bioRxiv2021

A tool for federated training of segmentation models on whole slide images.

The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN based models, but this is hindered by the logistical challenges of sharing medical data. In this paper we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. We show that a federated trained model to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is comparable to a model trained by pooling the data on one server when tested on a fourth (holdout) institutions data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance.

pathology↗

bioRxiv2021

A user-friendly tool for cloud-based whole slide image segmentation, with examples from renal histopathology

BackgroundImage-based machine learning tools hold great promise for clinical applications in nephropathology and kidney research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often face prohibitive challenges in using these tools to their full potential, including the lack of technical expertise, suboptimal user interface, and limited computation power. MethodsWe have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. ResultsBy segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. ConclusionThe ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain. Histo-Cloud will greatly accelerate and facilitate the generation of datasets for machine learning in the analysis of kidney histology, empowering computationally novice end-users to conduct deep feature analysis of tissue slides.

pathology↗