Modality Conversion from Pathological Image to Ultrasonic Image Using Convolutional Neural Network

Abstract

Relation analysis between physical properties and microstructure of the human tissue has been widely conducted. In particular, the relationships between acoustic parameters and the microstructure of the human brain fall within the scope of our research. In order to analyze the relationship between physical properties and microstructure of the human tissue, accurate image registration is required. To observe the microstructure of the tissue, pathological (PT) image, which is an optical image capturing a thinly sliced specimen has been generally used. However, spatial resolutions and image features of PT image are markedly different from those of other image modalities. This study proposes a modality conversion method from PT image to ultrasonic (US) image including downscale process using convolutional neural network (CNN). Namely, constructed conversion model estimates the US from patch image of PT image. The proposed method was applied to the PT images and we confirmed that the converted PT images were similar to the US images from visual assessment. Image registration was then performed with converted PT and US images measuring the consecutive pathological specimens. Successful registration results were obtained in every pair of the images.