title: Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans.
abstract: The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of subsolid nodules in clinical routine.
authors.affiliation: Fraunhofer MEVIS, Universitaetsallee 29, 28359 Bremen, Germany.
authors.forename: B C
authors.initials: BC
authors.lastname: Lassen
authors.forename: C
authors.initials: C
authors.lastname: Jacobs
authors.forename: J-M
authors.initials: JM
authors.lastname: Kuhnigk
authors.forename: B
authors.initials: B
authors.lastname: van Ginneken
authors.forename: E M
authors.initials: EM
authors.lastname: van Rikxoort
mesh_terms: D002138:Calibration; D016208:Databases, Factual; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging*; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D011857:Radiographic Image Interpretation, Computer-Assisted; D013902:Radiography, Thoracic / Q000379:methods; D015203:Reproducibility of Results; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*