Multiresolution semantic segmentation of biological structures in digital histopathology.

Purpose: Semantic segmentation in high-resolution, histopathology whole slide images (WSIs) is an important fundamental task in various pathology applications. Convolutional neural networks (CNN) are the state-of-the-art approach for image segmentation. A patch-based CNN approach is often employed because of the large size of WSIs; however, segmentation performance is sensitive to the field-of-view and resolution of the input patches, and balancing the trade-offs is challenging when there are drastic size variations in the segmented structures.

Automated detection of microscopic placental features indicative of maternal vascular malperfusion using machine learning.

Introduction: Hypertensive disorders of pregnancy (HDP) and fetal growth restriction (FGR) are common obstetrical complications, often with pathological features of maternal vascular malperfusion (MVM) in the placenta. Currently, clinical placental pathology methods involve a manual visual examination of histology sections, a practice that can be resource-intensive and demonstrates moderate-to-poor inter-pathologist agreement on diagnostic outcomes, dependant on the degree of pathologist sub-specialty training.

Methods: This study aims to apply machine learning (ML) feature extraction methods to classify digital images of placental histopathology specimens, collected from cases of HDP [pregnancy induced hypertension (PIH), preeclampsia (PE), PE + FGR], normotensive FGR, and healthy pregnancies, according to the presence or absence of MVM lesions.

Fully automated estimation of the mean linear intercept in histopathology images of mouse lung tissue.

The mean linear intercept (MLI) score is a common metric for quantification of injury in lung histopathology images. The automated estimation of the MLI score is a challenging task because it requires accurate segmentation of different biological components of the lung tissue. Therefore, the most widely used approaches for MLI quantification are based on manual/semi-automated assessment of lung histopathology images, which can be expensive and time-consuming.

Automated segmentation of villi in histopathology images of placenta.

Purpose: Manual analysis of clinical placenta pathology samples under the microscope is a costly and time-consuming task. Computer-aided diagnosis might offer a means to obtain fast and reliable results and also substantially reduce inter- and intra-rater variability. Here, we present a fully automated segmentation method that is capable of distinguishing the complex histological features of the human placenta (i.