Robotic Manipulation of Pathological Slides Powered by Deep Learning and Classical Image Processing

Digital pathology has many advantages, so the need for digitizing already existing archives naturally arises. However, the fact that there is no standard way of storing pathology archives makes it difficult to provide an automated solution. In this paper, we tackle this problem with a robotic system, which uses a deep convolutional neural network and traditional image processing methods to automatically detect and localize the pathology samples and perform pick and place to organize the samples in a rack that can be directly inserted into the whole slide imaging (WSI) scanner. We were able to achieve a 90% success rate for the pick and place process. This paper introduces the hardware setup and software components that we used for our system and briefly explains the detection procedure.