Pulmonary nodules (AKA lung nodules) are small masses (up to 30mm) of tissue surrounded by pulmonary parenchyma. They are quite common finding on computerized tomography (CT) scans, and although most lung nodules are benign, some are cancerous. Some of the characteristics of the nodules may indicate high suspicion of malignancy such as large size, irregular/spiculated borders or inhomogeneous density. In that case, a tissue biopsy (percutaneous or via bronchoscopy) might be indicated.
Nodules volume and borders assessment are important in cancer diagnosis and staging. CT offers very reliable imaging for evaluation and follow-up of nodule size, growth, and location. It also allows visualization of nodule attenuation (density) and borders.
Nodule segmentation can also assist the physician during the biopsy, whether it is done via bronchoscopy or percutaneously, by helping the physician choose the best trajectory, hence minimizing complications.
Automated lung nodules segmentation
RSIP Vision has developed a state-of-the-art module for detection of lung nodules. Traditional methods based on classic grey level based techniques would not work in this challenging task. The AI methods were used to address various critical issues to distinguish between nodules and blood vessels that have very similar appearance in grey scale. Hence, the appearance of nodules might be very different between the solid ones and the Ground-Glass opacity ones.
The Deep Learning methods are very versatile and include powerful methods like U-Net and Mask-R CNN that can derive maximum information from the scan in the 3D and the environment of the nodule as well. Much care is taken to furnish the most precise annotated images to the system. During the training phase, the weights of the network get adjusted and refined for the specific task at hand. The result is a very accurate lung nodules segmentation with Deep Learning, that can give you much better results than the ones you currently have. Contact now our engineers to learn more about the automated pulmonary nodules segmentation!