Artif Intell Med - A segmentation framework for abdominal organs from CT scans.

Tópicos

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Resumo

JECTIVE: Computed tomography images are becoming an invaluable mean for abdominal organ investigation. In the field of medical image processing, some of the current interests are the automatic diagnosis of liver, spleen, and kidney pathologies, and the 3D volume rendering of these abdominal organs. Their automatic segmentation is the first and fundamental step in all these studies, but it is still an open problem.METHODS: In this paper we propose a fully automatic, gray-level based segmentation framework based on a multiplanar fast marching method. The proposed segmentation scheme is general, and employs only established and not critical anatomical knowledge. For this reason, it can be easily adapted to segment different abdominal organs, by overcoming problems due to the high inter- and intra-patient gray-level, and shape variabilities; the extracted volumes are then combined to produce the final results.RESULTS: The system has been evaluated by computing the symmetric volume overlap (SVO) between the automatically segmented (liver and spleen) volumes and the volumes manually traced by radiological experts. The test dataset is composed of 60 images, where 40 images belong to a private dataset, and 20 images to a public one. Liver segmentation has achieved an average SVO congruent with94, which is comparable to the mean intra- and inter-personal variation (96). Spleen segmentation achieves similar, promising results (SVO congruent with93). The comparison of these results with those achieved by active contour models (SVO congruent with90), and topology adaptive snakes (SVO congruent with92) proves the efficacy of our system.CONCLUSIONS: The described segmentation method is a general framework that can be adapted to segment different abdominal organs, achieving promising segmentation results. It has to be noted that its performance could be further improved by incorporating shape based rules.

Resumo Limpo

jectiv comput tomographi imag becom invalu mean abdomin organ investig field medic imag process current interest automat diagnosi liver spleen kidney patholog d volum render abdomin organ automat segment first fundament step studi still open problemmethod paper propos fulli automat graylevel base segment framework base multiplanar fast march method propos segment scheme general employ establish critic anatom knowledg reason can easili adapt segment differ abdomin organ overcom problem due high inter intrapati graylevel shape variabl extract volum combin produc final resultsresult system evalu comput symmetr volum overlap svo automat segment liver spleen volum volum manual trace radiolog expert test dataset compos imag imag belong privat dataset imag public one liver segment achiev averag svo congruent compar mean intra interperson variat spleen segment achiev similar promis result svo congruent comparison result achiev activ contour model svo congruent topolog adapt snake svo congruent prove efficaci systemconclus describ segment method general framework can adapt segment differ abdomin organ achiev promis segment result note perform improv incorpor shape base rule

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