title: A segmentation framework for abdominal organs from CT scans.
abstract: OBJECTIVE
  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.
authors.affiliation: Dipartimento di Scienze dell'Informazione, Universitá degli Studi di Milano, Via Comelico 39/41, Milan, Italy.
authors.forename: Paola
authors.initials: P
authors.lastname: Campadelli
authors.forename: Elena
authors.initials: E
authors.lastname: Casiraghi
authors.forename: Stella
authors.initials: S
authors.lastname: Pratissoli
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D006801:Humans; D021621:Imaging, Three-Dimensional; D007668:Kidney / Q000000981:diagnostic imaging; D008099:Liver / Q000000981:diagnostic imaging; D008490:Medical Informatics; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D011379:Prognosis; D011857:Radiographic Image Interpretation, Computer-Assisted; D011860:Radiography, Abdominal / Q000379:methods*; D013154:Spleen / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed