Surgical planning in oncological liver surgery is based on the location of the 8 anatomical segments according to Couinaud's definition and tumors inside these structures. The detection of the boundaries between the segments is then the first step of the preoperative planning. The proposed method, devoted to binary images of livers segmented from CT-scans, has been designed to delineate these segments. It automatically detects a set of landmarks using a priori anatomical knowledge and differential geometry criteria. These landmarks are then used to position the Couinaud's segments. Validations performed on 7 clinical cases tend to prove that the method is reliable for most of these separation planes.
Phase-contrast magnetic resonance angiography (PC-MRA) can produce phase images which are 3-dimensional pictures of vascular structures.
However, it also provides magnitude images, containing anatomical - but no vascular - data.
Classically, algorithms dedicated to PC-MRA segmentation detect the cerebral vascular tree by only working on phase images.
We propose here a new approach for segmentation of cerebral blood vessels in PC-MRA using both types of images.
This approach is based on the hypothesis that a magnitude image contains anatomical information useful for vascular structures detection.
That information can then be transposed from a normal case to any patient image by image registration.
An atlas of the whole head has been developed in order to store such anatomical knowledge.
It divides a magnitude image into several "vascular areas", each one having specific vessel properties.
The atlas can be applied on any magnitude image of an entire or nearly entire head by deformable matching, thus helping to segment blood vessels from the associated phase image.
The segmentation method used afterwards is composed of a topology-conserving region growing algorithm using adaptative threshold values depending on the current region of the atlas.
This algorithm builds the arterial and venous trees by iteratively adding voxels which are selected according to their greyscale value and the variation of values in their neighborhood.
The topology conservation is guaranteed by only selecting simple points during the growing process.
The method has been performed on 15 PC-MRA's of the brain.
The results have been validated using MIP and 3D surface rendering visualization; a comparison to other results obtained without an atlas proves that atlas-based methods are an
effective way to optimize vascular segmentation strategies.