AN APPLICATION FOR SUPPORTING CT INTERPRETATION OF INTERSTITIAL PNEUMONIA

Abstract

Diagnosis of Interstitial pneumonia by systemic sclerosis utilizes manual CT interpretation, in order to judge state of the disease and, thus, supports by computers is expected for that interpretation. In this paper, we develop an application to support the diagnosis of CT images. At first, we construct a neural network to accurately and robustly extract lung regions and disease regions from CT images by using deep learning techniques. Then, ratios of the disease regions toward the lung regions on all CT images of several patients are computed, and they are compared with results of other medical tests. Consequently, DLCO has a relatively close relation with those ratios. Finally, some useful functions for post process are also developed and they are integrated into an application for trial uses in medical practice.