Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives.

Whole slide imaging enables the use of a wide array of digital image analysis tools that are revolutionizing pathology. Recent advances in digital pathology and deep convolutional neural networks have created an enormous opportunity to improve workflow efficiency, provide more quantitative, objective, and consistent assessments of pathology datasets, and develop decision support systems. Such innovations are already making their way into clinical practice. However, the progress of machine learning - in particular, deep learning (DL) - has been rather slower in nonclinical toxicology studies. Histopathology data from toxicology studies are critical during the drug development process that is required by regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Due to the high volume of slides routinely evaluated, low-throughput, or narrowly performing DL methods that may work well in small-scale diagnostic studies or for the identification of a single abnormality are tedious and impractical for toxicologic pathology. Furthermore, regulatory requirements around good laboratory practice are a major hurdle for the adoption of DL in toxicologic pathology. This paper reviews the major DL concepts, emerging applications, and examples of DL in toxicologic pathology image analysis. We end with a discussion of specific challenges and directions for future research.

Advances in imaging of vertebral and spinal cord injury.

BACKGROUND/OBJECTIVES: Imaging technology is an important part of the diagnosis and management of spinal trauma. Indications and findings in post-traumatic imaging of the vertebral column and spinal cord are reviewed. METHODS: An extensive literature review was performed on the imaging of vertebral and spinal cord injury. Relevant images from a Level I trauma center were included as examples. RESULTS: Imaging plays an important role in the evaluation of acute and chronic spinal injury. Spinal cord and soft-tissue injuries are best evaluated by magnetic resonance imaging (MRI), whereas spinal fractures are better characterized by computed tomography (CT). Vascular injuries can be evaluated using CT or MR angiography. CONCLUSIONS: Imaging using CT and MRI is essential in the management of spinal cord injuries, both in the acute and in the chronic settings. MRI shows the status of ligamentous integrity and visualizes internal derangement of the spinal cord. Vascular compromise can be diagnosed by MR and CT angiography. Plain radiography now has a more limited, adjunctive role, and the need for higher risk myelography has been minimized.