Int J Comput Assist Radiol Surg - Spatially adaptive active contours: a semi-automatic tumor segmentation framework.

Tópicos

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Resumo

RPOSE: Tumor segmentation constitutes a crucial step in simulating cancer growth and response to therapy. Incorporation of imaging data individualizes the simulation and assists clinical correlation with the predicted outcome. We adapted snakes to improve tumor segmentation including difficult cases with inherently inhomogeneous structure and poorly defined margins.METHODS: Snakes are flexible curves, based on the parameter-controlled deformation of an initial user-defined contour toward the boundary of the desired object, through the minimization of a suitable energy function. Although parameter-adjustment can yield fairly good results in homogeneous regions, traditional snakes often fail to provide an accurate segmentation result when both rigid and very elastic behavior is needed simultaneously to delineate the true outline of the tumor. We developed and tested a spatially adaptive active contour technique by introducing local snake bending, to improve traditional snakes performance for segmenting tumors. The key point of our method is the use of adaptable snake parameters, instead of constant ones, to adjust the bending of the curve according to the local edge characteristics. Our algorithm discriminates image regions according to underlying image features, such as gradient magnitude and corner strength. More specifically, it assigns each region a different "localized" set of parameters, one corresponding to a very flexible snake, and the other corresponding to a very rigid one, according to the local image characteristics.RESULTS: Qualitative results on more than 150 real MR images, as well as quantitative validation based on agreement with an expert clinician's annotations of the true tumor boundaries, demonstrate our approach is highly efficient compared to traditional active contours and region growing. Due to the use of adaptable parameters in the snake evolution process, our approach outperforms the other two methods, and consistently follows an expert's annotations. Statistical tests indicated significant difference between the results produced by our approach and two other algorithms traditional snakes and region growing, while multiple comparison showed that our method consistently outperformed those algorithms, with an average overlap of 89%, over the entire data set, while traditional snakes were at 82.5% and region growing at 59.2%. Furthermore, we performed several tests that demonstrate our method's stability to different initial contours, as well as, to lower resolution images.CONCLUSION: Our adaptive snake algorithm can spatially adapt to diverse image characteristics, producing outlines that mimic the true tumor boundaries. Results in MR datasets are very close to an expert clinician's intuition about the tumor boundaries.

Resumo Limpo

rpose tumor segment constitut crucial step simul cancer growth respons therapi incorpor imag data individu simul assist clinic correl predict outcom adapt snake improv tumor segment includ difficult case inher inhomogen structur poor defin marginsmethod snake flexibl curv base parametercontrol deform initi userdefin contour toward boundari desir object minim suitabl energi function although parameteradjust can yield fair good result homogen region tradit snake often fail provid accur segment result rigid elast behavior need simultan delin true outlin tumor develop test spatial adapt activ contour techniqu introduc local snake bend improv tradit snake perform segment tumor key point method use adapt snake paramet instead constant one adjust bend curv accord local edg characterist algorithm discrimin imag region accord under imag featur gradient magnitud corner strength specif assign region differ local set paramet one correspond flexibl snake correspond rigid one accord local imag characteristicsresult qualit result real mr imag well quantit valid base agreement expert clinician annot true tumor boundari demonstr approach high effici compar tradit activ contour region grow due use adapt paramet snake evolut process approach outperform two method consist follow expert annot statist test indic signific differ result produc approach two algorithm tradit snake region grow multipl comparison show method consist outperform algorithm averag overlap entir data set tradit snake region grow furthermor perform sever test demonstr method stabil differ initi contour well lower resolut imagesconclus adapt snake algorithm can spatial adapt divers imag characterist produc outlin mimic true tumor boundari result mr dataset close expert clinician intuit tumor boundari

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