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 res

SUBMITTER: Kalpathy-Cramer J 

PROVIDER: S-EPMC4942386 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature