title: A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.
abstract: Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 μl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.
authors: Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
authors: Jayashree
authors: J
authors: Kalpathy-Cramer
authors: Department of Radiology, Columbia University Medical Center, New York, NY, USA.
authors: Binsheng
authors: B
authors: Zhao
authors: Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA.
authors: Dmitry
authors: D
authors: Goldgof
authors: Departments of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
authors: Yuhua
authors: Y
authors: Gu
authors: Department of Radiology, Stanford University School of Medicine, James H. Clark Center S323 318 Campus Drive, Stanford, CA, 94305-5450, USA.
authors: Xingwei
authors: X
authors: Wang
authors: Department of Radiology, Columbia University Medical Center, New York, NY, USA.
authors: Hao
authors: H
authors: Yang
authors: Department of Radiology, Columbia University Medical Center, New York, NY, USA.
authors: Yongqiang
authors: Y
authors: Tan
authors: Departments of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
authors: Robert
authors: R
authors: Gillies
authors: Department of Radiology, Stanford University School of Medicine, James H. Clark Center S323 318 Campus Drive, Stanford, CA, 94305-5450, USA. snapel@stanford.edu.
authors: Sandy
authors: S
authors: Napel
mesh_terms: D000465:Algorithms; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000473:pathology; D014057:Tomography, X-Ray Computed; D047368:Tumor Burden
keywords: Computed tomography; Infrastructure; Lung cancer; Quantitative imaging; Segmentation