Fast interactive segmentation of the pulmonary lobes from thoracic computed tomography data

Abstract

Automated lung lobe segmentation methods often fail for challenging and clinically relevant cases with incomplete fissures or substantial amounts of pathology. We present a fast and intuitive method to interactively correct a given lung lobe segmentation or to quickly create a lobe segmentation from scratch based on a lung mask. A given lobar boundary is converted into a mesh by principal component analysis of 3D lobar boundary markers to obtain a plane where nodes correspond to the position of the markers. An observer can modify the mesh by drawing on 2D slices in arbitrary orientations. After each drawing, the mesh is immediately adapted in a 3D region around the user interaction. For evaluation we participated in the international lung lobe segmentation challenge LObe and lung analysis 2011 (LOLA11). Two observers applied the method to correct a given lung lobe segmentation obtained by a fully automatic method for all 55 CT scans of LOLA11. On average observer 1/2 required 8 ± 4/25 ± 12 interactions per case and took 1:30 ± 0:34/3:19 ± 1:29 min. The average distances to the reference segmentation were improved from an initial 2.68 ± 14.71 mm to 0.89 ± 1.63/0.74 ± 1.51 mm. In addition, one observer applied the proposed method to create a segmentation from scratch. This took 3:44 ± 0:58 minutes on average per case, applying an average of 20 ± 3 interactions to reach an average distance to the reference of 0.77 ± 1.14 mm. Thus, both the interactive corrections and the creation of a segmentation from scratch were feasible in a short time with excellent results and only little interaction. Since the mesh adaptation is independent of image features, the method can successfully handle patients with severe pathologies, provided that the human operator is capable of correctly indicating the lobar boundaries.

The Emerging Role of Quantification of Imaging for Assessing the Severity and Disease Activity of Emphysema, Airway Disease, and Interstitial Lung Disease

“…Patients with TLVR ≥350 mL had statistically significant improvement in respiratory function, exercise performance, and quality of life measures [60, 111-114]. More recent data support the use of quantitative CT measurements to screen in patients for further analysis and/or treatment with fissure completeness [56, 104, 113]. The QCT evaluation is noninvasive and thus is increasingly used as the screening test of choice.…”

“…In this prespecified analysis, patients with a fissure integrity score >90% of the fissure abutting the treatment lobe were shown to have significant improvement in their forced expiratory volume in 1 s (FEV1) compared to those whose FIS was <90% [41]. The majority of automated fissure detection methods use feature descriptors targeted toward normal fissure anatomy [46][47][48][49][50][51][52][53][54][55][56]. Validation of fissure integrity, as a marker for collateral flow between lobes, has been confirmed both indirectly by improved treatment outcome as well as directly from local flow measurements within the lobes [57][58][59][60].…”

Role of Quantitative Computed Tomographic Scan Analysis in Lung Volume Reduction for Emphysema

“…Moreover, pathological conditions that result in incomplete or abnormal fissures could make segmentation even more difficult. Several studies were conducted applying different algorithms to overcome this limitation, all with good results [27, 39]. However, manual checking might still be required to confirm the results.…”

Pulmonary Lobe Segmentation in CT Images Based on Lung Anatomy Knowledge

“…Using a different strategy, Lo et al [9] used a fissure confidence function to verify pulmonary fissures; then, a surface evolution algorithm was applied for pulmonary lobe segmentation. Lassen-Schmidt et al [10] firstly selected a number of representative lobar boundary points; then, a plane was constructed by the given points to segment pulmonary lobes, but their algorithms [9,10] need manual interaction. Recently, Wei and Hu [11] used a texture analysis method based on the neural network and a stack projection to verify lobar fissures; then, a fissure surface model was used for pulmonary lobe segmentation.…”