title: Marker-controlled watershed for lymphoma segmentation in sequential CT images.
abstract: Segmentation of lymphoma containing lymph nodes is a difficult task because of multiple variables associated with the tumor's location, intensity distribution, and contrast to its surrounding tissues. In this paper, we present a reliable and practical marker-controlled watershed algorithm for semi-automated segmentation of lymphoma in sequential CT images. Robust determination of internal and external markers is the key to successful use of the marker-controlled watershed transform in the segmentation of lymphoma and is the focus of this work. The external marker in our algorithm is the circle enclosing the lymphoma in a single slice. The internal marker, however, is determined automatically by combining techniques including Canny edge detection, thresholding, morphological operation, and distance map estimation. To obtain tumor volume, the segmented lymphoma in the current slice needs to be propagated to the adjacent slice to help determine the external and internal markers for delineation of the lymphoma in that slice. The algorithm was applied to 29 lymphomas (size range, 9-53 mm in diameter; mean, 23 mm) in nine patients. A blinded radiologist manually delineated all lymphomas on all slices. The manual result served as the "gold standard" for comparison. Several quantitative methods were applied to objectively evaluate the performance of the segmentation algorithm. The algorithm received a mean overlap, overestimation, and underestimation ratios of 83.2%, 13.5%, and 5.5%, respectively. The mean average boundary distance and Hausdorff boundary distance were 0.7 and 3.7 mm. Preliminary results have shown the potential of this computer algorithm to allow reliable segmentation and quantification of lymphomas on sequential CT images.
authors.affiliation: Medical Physics Department, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, New York 10021, USA. yanj12@mskcc.org
authors.forename: Jiayong
authors.initials: J
authors.lastname: Yan
authors.forename: Binsheng
authors.initials: B
authors.lastname: Zhao
authors.forename: Liang
authors.initials: L
authors.lastname: Wang
authors.forename: Andrew
authors.initials: A
authors.lastname: Zelenetz
authors.forename: Lawrence H
authors.initials: LH
authors.lastname: Schwartz
mesh_terms: D000465:Algorithms; D001331:Automation; D003287:Contrast Media / Q000494:pharmacology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008223:Lymphoma / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000473:pathology*; D015233:Models, Statistical; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*
chemical_list: D003287:Contrast Media