Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach

—The accurate segmentation of lung lesions from computed tomography (CT) scans is important for lung cancer research and can offer valuable information for clinical diagnosis and treatment. However, it is challenging to achieve a fully automatic lesion detection and segmentation with acceptable accuracy due to the heterogeneity of lung lesions. Here, we propose a novel toboggan based growing automatic segmentation approach (TBGA) with a three-step framework, which are automatic initial seed point selection, multi-constraints 3D lesion extraction and the final lesion refinement. The new approach does not require any human interaction or training dataset for lesion detection, yet it can provide a high lesion detection sensitivity (96.35%) and a comparable segmentation accuracy with manual segmentation (P > 0.05), which was proved by a series assessments using the LIDC-IDRI dataset (850 lesions) and in-house clinical dataset (121 lesions). We also compared TBGA with commonly used lev...

• Type: Journal
• Year: 2016
• Where: TMI
• Authors: Jiangdian Song, Caiyun Yang, Li Fan, Kun Wang, Feng Yang, Shiyuan Liu, Jie Tian
• Keywords: Knowledge Management, TMI 2016