Image Segmentation of the Pulmonary Acinus Imaged by Synchrotron X-Ray Tomography

Abstract

Pulmonary acinus represents the gas exchange unit which includes branches of the terminal bronchiole, alveolar ducts, alveolar sacs, alveoli and associated blood vessels. Over the past few decades, many results related to the fluid mechanics characterizing pulmonary acinus of the lungs have been reported. In order to describe a micromechanics in 3D acinar micro-architecture and airflow through it, 3D reconstruction of parenchyma with computational fluid dynamics plays an important role. For the reliable 3D model, precise image segmentation of the stacked 2D images is a necessary pre-step. However, in most cases this step is neglected and the classic threshold segmentation is applied. Convolutional neural networks proved to be very successful in image classification and object detection, and in the field of medical image segmentation U-Net architecture showed very good performance. In this paper, automatic pulmonary acinus lung field segmentation has been performed using U-Net based deep convolutional network. Our proposed model has been evaluated on the images of rat lungs imaged by synchrotron radiation-based X-ray tomographic microscopy (SRXTM). The experimental results show that our model outperforms the baseline models.