Abstract
Constipation has multiple symptoms and pathologies, and the treatment depends on the cause of constipation. Quantification of the amount and location of gas and stool in radiographic images is effective to select the appropriate treatment. We are developing a segmentation method of gas and stool regions in radiographic images using U-Net. The purpose of this paper is to investigate the effectiveness of pre-learning and backbone in U-Net. The gas volume score (GVS) is an index for quantitatively evaluating the quantity of intestinal gas. As well as GVS, we propose a stool volume score (SVS), and the joint volume score (JVS) which quantify the combined region. Experiments were conducted under two conditions: with/without a backbone, and with/without pre-training. The experimental results showed that the proposed method with vgg16 backbone and fine-tuning achieved a correlation coefficient of 0.901 for GVS, 0.618 for SVS, and 0.437 for JVS. DICE coefficients were 0.669 for the gas region, 0.523 for the stool region, and 0.646 for the combined gas and stool region. The extraction accuracy was best when both pre-training and backbone were used, indicating the usefulness of pre-training and VGG16 backbone in this method.
