Comput Methods Programs Biomed - Snake model-based lymphoma segmentation for sequential CT images.

Tópicos

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Resumo

The measurement of the size of lesions in follow-up CT examinations of cancer patients is important to evaluate the success of treatment. This paper presents an automatic algorithm for identifying and segmenting lymph nodes in CT images across longitudinal time points. Firstly, a two-step image registration method is proposed to locate the lymph nodes including coarse registration based on body region detection and fine registration based on a double-template matching algorithm. Then, to make the initial segmentation approximate the boundaries of lymph nodes, the initial image registration result is refined with intensity and edge information. Finally, a snake model is used to evolve the refined initial curve and obtain segmentation results. Our algorithm was tested on 26 lymph nodes at multiple time points from 14 patients. The image at the earlier time point was used as the baseline image to be used in evaluating the follow-up image, resulting in 76 total test cases. Of the 76 test cases, we made a 76 (100%) successful detection and 38/40 (95%) correct clinical assessment according to Response Evaluation Criteria in Solid Tumors (RECIST). The quantitative evaluation based on several metrics, such as average Hausdorff distance, indicates that our algorithm is produces good results. In addition, the proposed algorithm is fast with an average computing time 2.58s. The proposed segmentation algorithm for lymph nodes is fast and can achieve high segmentation accuracy, which may be useful to automate the tracking and evaluation of cancer therapy.

Resumo Limpo

measur size lesion followup ct examin cancer patient import evalu success treatment paper present automat algorithm identifi segment lymph node ct imag across longitudin time point first twostep imag registr method propos locat lymph node includ coars registr base bodi region detect fine registr base doubletempl match algorithm make initi segment approxim boundari lymph node initi imag registr result refin intens edg inform final snake model use evolv refin initi curv obtain segment result algorithm test lymph node multipl time point patient imag earlier time point use baselin imag use evalu followup imag result total test case test case made success detect correct clinic assess accord respons evalu criteria solid tumor recist quantit evalu base sever metric averag hausdorff distanc indic algorithm produc good result addit propos algorithm fast averag comput time s propos segment algorithm lymph node fast can achiev high segment accuraci may use autom track evalu cancer therapi

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