| Automated temporal tracking and segmentation of lymphoma on serial CT examinations. | |
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MedLine Citation:
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PMID: 22047352 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Purpose: It is challenging to reproducibly measure and compare cancer lesions on numerous follow-up studies; the process is time-consuming and error-prone. In this paper, we show a method to automatically and reproducibly identify and segment abnormal lymph nodes in serial computed tomography (CT) exams.Methods: Our method leverages initial identification of enlarged (abnormal) lymph nodes in the baseline scan. We then identify an approximate region for the node in the follow-up scans using nonrigid image registration. The baseline scan is also used to locate regions of normal, non-nodal tissue surrounding the lymph node and to map them onto the follow-up scans, in order to reduce the search space to locate the lymph node on the follow-up scans. Adaptive region-growing and clustering algorithms are then used to obtain the final contours for segmentation. We applied our method to 24 distinct enlarged lymph nodes at multiple time points from 14 patients. The scan at the earlier time point was used as the baseline scan to be used in evaluating the follow-up scan, resulting in 70 total test cases (e.g., a series of scans obtained at 4 time points results in 3 test cases). For each of the 70 cases, a "reference standard" was obtained by manual segmentation by a radiologist. Assessment according to response evaluation criteria in solid tumors (RECIST) using our method agreed with RECIST assessments made using the reference standard segmentations in all test cases, and by calculating node overlap ratio and Hausdorff distance between the computer and radiologist-generated contours.Results: Compared to the reference standard, our method made the correct RECIST assessment for all 70 cases. The average overlap ratio was 80.7 ± 9.7% s.d., and the average Hausdorff distance was 3.2 ± 1.8 mm s.d. The concordance correlation between automated and manual segmentations was 0.978 (95% confidence interval 0.962, 0.984). The 100% agreement in our sample between our method and the standard with regard to RECIST classification suggests that the true disagreement rate is no more than 6%.Conclusions: Our automated lymph node segmentation method achieves excellent overall segmentation performance and provides equivalent RECIST assessment. It potentially will be useful to streamline and improve cancer lesion measurement and tracking and to improve assessment of cancer treatment response. |
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Authors:
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Jiajing Xu; Hayit Greenspan; Sandy Napel; Daniel L Rubin |
Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Medical physics Volume: 38 ISSN: 0094-2405 ISO Abbreviation: Med Phys Publication Date: 2011 Nov |
Date Detail:
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Created Date: 2011-11-03 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0425746 Medline TA: Med Phys Country: United States |
Other Details:
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Languages: eng Pagination: 5879 Citation Subset: IM |
Affiliation:
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Department of Electrical Engineering, Stanford University, Stanford, California 94305. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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