A segmentation framework for abdominal organs from CT scans
Artificial Intelligence in Medicine
Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy
Computer Methods and Programs in Biomedicine
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Computed tomography (CT) 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 the abdominal organs. The first and fundamental step in all these studies is the automatic organs segmentation, that is still an open problem. In this paper we propose a fully automatic gray level based segmentation framework that employs a fast marching technique; the proposed segmentation scheme is general, and employs only established and not critical anatomical knowledge. For this reason, it can be easily adapted to separately 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 achieve robust results. The system performance has been evaluated on the data of 40 patients, by comparing the automatically detected organ volumes to the organ boundaries manually traced by three experts. The good quality of the achieved results is proved by the fact that they are comparable to the inter and intra personal variability of the manual segmentation produced by experts.