Analysis of cellularity in H&E-stained rat bone marrow tissue via deep learning.

Our objective was to develop an automated deep-learning-based method to evaluate cellularity in rat bone marrow hematoxylin and eosin whole slide images for preclinical safety assessment. We trained a shallow CNN for segmenting marrow, 2 Mask R-CNN models for segmenting megakaryocytes (MKCs), and small hematopoietic cells (SHCs), and a SegNet model for segmenting red blood cells. We incorporated the models into a pipeline that identifies and counts MKCs and SHCs in rat bone marrow.

An automated mapping method for Nissl-stained mouse brain histologic sections.

Background: Histologic evaluation of the central nervous system is often a critical endpoint in in vivo efficacy studies, and is considered the essential component of neurotoxicity assessment in safety studies. Automated image analysis is a powerful tool that can radically reduce the workload associated with evaluating brain histologic sections.

New Method: We developed an automated brain mapping method that identifies neuroanatomic structures in mouse histologic coronal brain sections.

Interactive specification of regions of interest on brain surfaces.

We describe Surface Editor-a tool for interactive specification of regions of interest (ROIs) on brain surfaces. The tool allows users to define subsurfaces by tracing around areas within a triangle-mesh brain surface. The input to the program is a triangle-mesh representation of a brain volume and a set of user-defined input points on the mesh.

Semiautomated segmentation of blood vessels using ellipse-overlap criteria: method and comparison to manual editing.

Two-dimensional intensity-based methods for the segmentation of blood vessels from computed-tomography-angiography data often result in spurious segments that originate from other objects whose intensity distributions overlap with those of the vessels. When segmented images include spurious segments, additional methods are required to select segments that belong to the target vessels. We describe a method that allows experts to select vessel segments from sequences of segmented images with little effort.