Segmentation of nodules on chest computed tomography for growth assessment

Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a real...

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Published in:	Medical physics (Lancaster) Vol. 31; no. 4; pp. 839 - 848
Main Authors:	Mullally, William, Betke, Margrit, Wang, Jingbin, Ko, Jane P.
Format:	Journal Article
Language:	English
Published:	United States American Association of Physicists in Medicine 01.04.2004
Subjects:	3D algorithms Algorithms Artificial Intelligence cancer Computed radiography computed tomography computer aided analysis computerised tomography computer‐aided diagnosis Humans image segmentation Imaging, Three-Dimensional lung lung cancer medical image processing Neoplasm Staging - methods Pattern Recognition, Automated phantom study phantoms Phantoms, Imaging Physicists Radiographic Image Interpretation, Computer-Assisted - instrumentation Radiographic Image Interpretation, Computer-Assisted - methods Radiography, Thoracic - instrumentation Radiography, Thoracic - methods Radiologists Reproducibility of Results Sensitivity and Specificity shape analysis Solitary Pulmonary Nodule - classification Solitary Pulmonary Nodule - diagnostic imaging Solitary Pulmonary Nodule - pathology tumours volume effects lung cancer phantom study computed tomography volume effects 3D algorithms image segmentation shape analysis computer-aided diagnosis
ISSN:	0094-2405, 2473-4209
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Abstract	Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a realistic chest phantom to evaluate their performance with respect to specific nodule characteristics. The segmentation methods were also tested on sequential CT scans of patients. The methods’ estimation of nodule growth were compared to the volume change calculated by a chest radiologist. The best method segmented nodules on average 43% smaller or larger than the actual nodule when errors were computed across all nodule variations on the phantom. Some methods achieved smaller errors when examined with respect to certain nodule properties. In particular, on the phantom individual methods segmented solid nodules to within 23% of their actual size and nodules with 60.7 mm3 volumes to within 14%. On the clinical data, none of the methods examined showed a statistically significant difference in growth estimation from the radiologist.
AbstractList	Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a realistic chest phantom to evaluate their performance with respect to specific nodule characteristics. The segmentation methods were also tested on sequential CT scans of patients. The methods' estimation of nodule growth were compared to the volume change calculated by a chest radiologist. The best method segmented nodules on average 43% smaller or larger than the actual nodule when errors were computed across all nodule variations on the phantom. Some methods achieved smaller errors when examined with respect to certain nodule properties. In particular, on the phantom individual methods segmented solid nodules to within 23% of their actual size and nodules with 60.7 mm3 volumes to within 14%. On the clinical data, none of the methods examined showed a statistically significant difference in growth estimation from the radiologist. Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a realistic chest phantom to evaluate their performance with respect to specific nodule characteristics. The segmentation methods were also tested on sequential CT scans of patients. The methods' estimation of nodule growth were compared to the volume change calculated by a chest radiologist. The best method segmented nodules on average 43% smaller or larger than the actual nodule when errors were computed across all nodule variations on the phantom. Some methods achieved smaller errors when examined with respect to certain nodule properties. In particular, on the phantom individual methods segmented solid nodules to within 23% of their actual size and nodules with 60.7 mm 3 volumes to within 14%. On the clinical data, none of the methods examined showed a statistically significant difference in growth estimation from the radiologist. Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a realistic chest phantom to evaluate their performance with respect to specific nodule characteristics. The segmentation methods were also tested on sequential CT scans of patients. The methods' estimation of nodule growth were compared to the volume change calculated by a chest radiologist. The best method segmented nodules on average 43% smaller or larger than the actual nodule when errors were computed across all nodule variations on the phantom. Some methods achieved smaller errors when examined with respect to certain nodule properties. In particular, on the phantom individual methods segmented solid nodules to within 23% of their actual size and nodules with 60.7 mm3 volumes to within 14%. On the clinical data, none of the methods examined showed a statistically significant difference in growth estimation from the radiologist.Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a realistic chest phantom to evaluate their performance with respect to specific nodule characteristics. The segmentation methods were also tested on sequential CT scans of patients. The methods' estimation of nodule growth were compared to the volume change calculated by a chest radiologist. The best method segmented nodules on average 43% smaller or larger than the actual nodule when errors were computed across all nodule variations on the phantom. Some methods achieved smaller errors when examined with respect to certain nodule properties. In particular, on the phantom individual methods segmented solid nodules to within 23% of their actual size and nodules with 60.7 mm3 volumes to within 14%. On the clinical data, none of the methods examined showed a statistically significant difference in growth estimation from the radiologist.
Author	Ko, Jane P. Betke, Margrit Wang, Jingbin Mullally, William
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Cites_doi	10.1118/1.1515762 10.1016/S0140-6736(99)06093-6 10.1109/42.650879 10.1016/S1076-6332(03)80115-0 10.1118/1.598605 10.1097/00004424-199404000-00013 10.1109/42.932744 10.1016/S0033-8389(22)00407-9 10.1148/radiology.218.1.r01ja39267 10.1117/1.602176 10.1118/1.1544679 10.1007/3-540-45468-3_13 10.1200/JCO.2000.18.10.2179 10.1148/radiology.217.1.r00oc33251 10.1148/radiology.212.2.r99au33561 10.3322/canjclin.49.1.8 10.1117/12.348494 10.1109/TMI.2003.817785 10.1148/radiographics.19.5.g99se181303 10.1016/S0033-8389(05)70177-9
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SubjectTerms	3D algorithms Algorithms Artificial Intelligence cancer Computed radiography computed tomography computer aided analysis computerised tomography computer‐aided diagnosis Humans image segmentation Imaging, Three-Dimensional lung lung cancer medical image processing Neoplasm Staging - methods Pattern Recognition, Automated phantom study phantoms Phantoms, Imaging Physicists Radiographic Image Interpretation, Computer-Assisted - instrumentation Radiographic Image Interpretation, Computer-Assisted - methods Radiography, Thoracic - instrumentation Radiography, Thoracic - methods Radiologists Reproducibility of Results Sensitivity and Specificity shape analysis Solitary Pulmonary Nodule - classification Solitary Pulmonary Nodule - diagnostic imaging Solitary Pulmonary Nodule - pathology tumours volume effects
Title	Segmentation of nodules on chest computed tomography for growth assessment
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