Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans

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.

[1]  Lubomir M. Hadjiiski,et al.  Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours. , 2006, Medical physics.

[2]  Heinz-Otto Peitgen,et al.  Advanced Segmentation Techniques for Lung Nodules, Liver Metastases, and Enlarged Lymph Nodes in CT Scans , 2009, IEEE Journal of Selected Topics in Signal Processing.

[3]  Mathias Prokop,et al.  Pulmonary ground-glass nodules: increase in mass as an early indicator of growth. , 2010, Radiology.

[4]  J. Austin,et al.  Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. , 2005, Radiology.

[5]  Shoji Kido,et al.  Automatic segmentation of pulmonary nodules on CT images by use of NCI lung image database consortium , 2006, SPIE Medical Imaging.

[6]  Paola Coan,et al.  X-ray phase-contrast imaging: from pre-clinical applications towards clinics , 2013, Physics in medicine and biology.

[7]  Donato Cascio,et al.  Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models , 2012, Comput. Biol. Medicine.

[8]  Jun Tan,et al.  Automated segmentation of pulmonary nodule depicted on CT images , 2011, Medical Imaging.

[9]  Anthony P. Reeves,et al.  Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images , 2003, IEEE Transactions on Medical Imaging.

[10]  Marcos Salganicoff,et al.  Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models , 2011, Medical Image Anal..

[11]  Guido Valli,et al.  3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Images , 2008, IEEE Transactions on Information Technology in Biomedicine.

[12]  M. L. R. D. Christenson,et al.  Guidelines for Management of Small Pulmonary Nodules Detected on CT Scans: A Statement From the Fleischner Society , 2006 .

[13]  L. Schwartz,et al.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.

[14]  Guozhen Zhang,et al.  Automatic Segmentation of Ground-Glass Opacities in Lung CT Images by Using Markov Random Field-Based Algorithms , 2012, Journal of Digital Imaging.

[15]  Zohreh Azimifar,et al.  Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system , 2013, Comput. Biol. Medicine.

[16]  Tiantian Zhang,et al.  A computer-based method of segmenting ground glass nodules in pulmonary CT images: comparison to expert radiologists' interpretations , 2005, SPIE Medical Imaging.

[17]  Stefano Diciotti,et al.  The ${LoG}$ Characteristic Scale: A Consistent Measurement of Lung Nodule Size in CT Imaging , 2010, IEEE Transactions on Medical Imaging.

[18]  Jamshid Dehmeshki,et al.  Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach , 2008, IEEE Transactions on Medical Imaging.

[19]  Richard C. Pais,et al.  The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. , 2011, Medical physics.

[20]  O. Miettinen,et al.  CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. , 2002, AJR. American journal of roentgenology.

[21]  R. Engelmann,et al.  Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique. , 2007, Medical physics.

[22]  Aly A. Farag,et al.  A Novel Approach for Lung Nodules Segmentation in Chest CT Using Level Sets , 2013, IEEE Transactions on Image Processing.

[23]  L. Schwartz,et al.  Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field. , 2013, Medical physics.

[24]  Heinz-Otto Peitgen,et al.  Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans , 2006, IEEE Transactions on Medical Imaging.

[25]  Jianhua Xuan,et al.  Multi-level Ground Glass Nodule Detection and Segmentation in CT Lung Images , 2009, MICCAI.

[26]  N. Müller,et al.  Fleischner Society: glossary of terms for thoracic imaging. , 2008, Radiology.

[27]  Rickard Carlsson,et al.  Experiment 1 - 4 , 2016 .