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Automatic liver segmentation from abdominal Computed Tomography (CT) is an important step for hepatic disease diagnosis. It is a challenging task owing to the similarity between liver and its adjacent organs and the low contrast of liver texture (e.g. tumors and blood veins). In this paper, we propose a cascaded structure to automatically segment liver in CT scans. First, we train a fully convolutional...
An automatic multi-organ segmentation method using hierarchical-shape-prior guided level sets is proposed. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, so that major structures with less population variety are at the top and smaller structures with higher irregularities are linked at a lower level. The segmentation is performed in a top-down...
In the recent years, reconstructing 3D liver and its vessels from abdominal CT volume images becomes an inevitable and necessary research field. In this paper, a method of 3D reconstruction of liver with its vessels has been implemented, which involves volume preprocessing, de-noising, segmentation, contouring, and combination of different modalities. An advanced liver segmentation algorithms have...
In this paper, we propose an interactive segmentation method to apply user information during the segmentation of a specific anatomic structure. This method is formulated to use belief propagation to minimize a global cost function according to local level sets. The propagation starts with one user labeled point, and iteratively extends the user information from the labeled pixel to its neighborhood...
This paper presents a new automatic initialization procedure for a level-set based segmentation algorithm that works on all slices for a given CT dataset. Level set segmentation algorithms provide promising results, are robust to dataset variations and do not require prior training. As such, they can be reliably used for segmentation of major organs in abdominal CT scans. However, level set algorithms...
Liver segmentation on computed tomography (CT) slices is a challenging task because the images are often corrupted by noise and sampling artifacts. Recent years fast marching method (FMM) has been introduced into the image segmentation domain and proved to have advantage in blur edge detection. When apply the FMM to the segmentation of liver CT slice, to attain the completely liver shape, an over-segmentation...
We present an algorithm for the segmentation of the liver in 2-D computed tomography slice images. The basis for our algorithm is an implicit active shape model. In order to detect the liver boundary and guide the shape model deformation, a boundary classifier has been integrated into the implicit framework in a novel manner. The accuracy of the algorithm has been evaluated for 20 test cases including...
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