An end-to-end pipeline based on open source deep learning tools for reliable analysis of complex 3D images of ovaries

Phenotypic analysis of complex bioengineered 3D models

With advances in underlying technologies such as complex multicellular systems, synthetic materials, and bioengineering techniques, we can now generate in vitro miniaturized human tissues that recapitulate the organotypic features of normal or diseased tissues. Importantly, these 3D culture models have increasingly provided experimental access to diverse and complex tissues architectures and their morphogenic assembly in vitro. This review presents an analytical toolbox for biological researchers using 3D modeling technologies through which they can find a collation of currently available methods to phenotypically assess their 3D models in their normal state as well as their response to therapeutic or pathological agents.

Tissue clearing and imaging approaches for in toto analysis of the reproductive system†

New microscopy techniques in combination with tissue clearing protocols and emerging analytical approaches have presented researchers with the tools to understand dynamic biological processes in a three-dimensional context. This paves the road for the exploration of new research questions in reproductive biology, for which previous techniques have provided only approximate resolution. These new methodologies now allow for contextualized analysis of far-larger volumes than was previously possible. Tissue optical clearing and three-dimensional imaging techniques posit the bridging of molecular mechanisms, macroscopic morphogenic development, and maintenance of reproductive function into one cohesive and comprehensive understanding of the biology of the reproductive system. In this review, we present a survey of the various tissue clearing techniques and imaging systems, as they have been applied to the developing and adult reproductive system. We provide an overview of tools available for analysis of experimental data, giving particular attention to the emergence of artificial intelligence-assisted methods and their applicability to image analysis. We conclude with an evaluation of how novel image analysis approaches that have been applied to other organ systems could be incorporated into future experimental evaluation of reproductive biology.

OoCount: A Machine-Learning Based Approach to Mouse Ovarian Follicle Counting and Classification

The number and distribution of ovarian follicles in each growth stage provides a reliable readout of ovarian health and function. Leveraging techniques for three-dimensional (3D) imaging of ovaries in toto has the potential to uncover total, accurate ovarian follicle counts. However, because of the size and holistic nature of these images, counting oocytes is time consuming and difficult. The advent of deep-learning algorithms has allowed for the rapid development of ultra-fast, automated methods to analyze microscopy images. In recent years, these pipelines have become more user-friendly and accessible to non-specialists. We used these tools to create OoCount, a high-throughput, open-source method for automatic oocyte segmentation and classification from fluorescent 3D microscopy images of whole mouse ovaries using a deep-learning convolutional neural network (CNN) based approach. We developed a fast tissue-clearing and spinning disk confocal-based imaging protocol to obtain 3D images of whole mount perinatal and adult mouse ovaries. Fluorescently labeled oocytes from 3D images of ovaries were manually annotated in Napari to develop a machine learning training dataset. This dataset was used to retrain StarDist using a CNN within DL4MicEverywhere to automatically label all oocytes in the ovary. In a second phase, we utilize Accelerated Pixel and Object Classification, a Napari plugin, to classify labeled oocytes and sort them into growth stages. Here, we provide an end-to-end protocol for producing high-quality 3D images of the perinatal and adult mouse ovary, obtaining follicle counts and staging. We also demonstrate how to customize OoCount to fit images produced in any lab. Using OoCount, we can obtain accurate counts of oocytes in each growth stage in the perinatal and adult ovary, improving our ability to study ovarian function and fertility.

Summary sentence

This protocol introduces OoCount, a high-throughput, open-source method for automatic oocyte segmentation and classification from fluorescent 3D microscopy images of whole mouse ovaries using a machine learning-based approach.

Predicting acute pancreatitis severity with enhanced computed tomography scans using convolutional neural networks

This study aimed to evaluate acute pancreatitis (AP) severity using convolutional neural network (CNN) models with enhanced computed tomography (CT) scans. Three-dimensional DenseNet CNN models were developed and trained using the enhanced CT scans labeled with two severity assessment methods: the computed tomography severity index (CTSI) and Atlanta classification. Each labeling method was used independently for model training and validation. Model performance was evaluated using confusion matrices, areas under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, F1 score, and respective macro-average metrics. A total of 1,798 enhanced CT scans met the inclusion criteria were included in this study. The dataset was randomly divided into a training dataset (n = 1618) and a test dataset (n = 180) with a ratio of 9:1. The DenseNet model demonstrated promising predictions for both CTSI and Atlanta classification-labeled CT scans, with accuracy greater than 0.7 and AUC-ROC greater than 0.8. Specifically, when trained with CT scans labeled using CTSI, the DenseNet model achieved good performance, with a macro-average F1 score of 0.835 and a macro-average AUC-ROC of 0.980. The findings of this study affirm the feasibility of employing CNN models to predict the severity of AP using enhanced CT scans.