Deep Learning-Based Segmentation and Quantification in Experimental Kidney Histopathology.

Background: Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation.

Development and Evaluation of a Novel Curved Biopsy Device for CT-Guided Biopsy of Lesions Unreachable Using Standard Straight Needle Trajectories.

Purpose: To evaluate the feasibility of a novel curved CT-guided biopsy needle prototype with shape memory to access otherwise not accessible biopsy targets.

Methods And Materials: A biopsy needle curved by 90° with specific radius was designed. It was manufactured using nitinol to acquire shape memory, encased in a straight guiding trocar to be driven out for access of otherwise inaccessible targets.