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Biomedical subjects

Hongjian Shi

Publications and source records attributed to Hongjian Shi.

2 recordsLinked to original sources

Validation framework of the finite element modeling of liver tissue.

In this work, we aim at validating some soft tissue deformation models using high resolution Micro Computed Tomography (Micro-CT) and medium resolution Cone-Beam CT (CBCT) images. These imaging techniques play a key role in detecting the tissue deformation details in the contact region between the tissue and the surgical tool (probe) even for small force loads, and provide good capabilities for creating accurate 3D models of tissues. Surgical simulations rely on accurate representation of the mechanical response of soft tissues subjected to surgical manipulations. Several finite element (F.E.) models have been suggested to characterize soft tissues. However, validating these models for specific tissues still remains a challenge. For our validation, ex vivo lamb liver is chosen to validate the linear elastic model (LEM), the linear viscoelastic model (LVEM), and the neo-Hooke hyperelastic model (NHM). We found that the LEM is more applicable to lamb liver than the LVEM for small force loads (< 40 g) and that the NHM is closer to reality than the LVEM for this same range of force loads.

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2D and 3D shape based segmentation using deformable models.

A novel shape based segmentation approach is proposed by modifying the external energy component of a deformable model. The proposed external energy component depends not only on the gray level of the images but also on the shape information which is obtained from the signed distance maps of objects in a given data set. The gray level distribution and the signed distance map of the points inside and outside the object of interest are accurately estimated by modelling the empirical density function with a linear combination of discrete Gaussians (LCDG) with positive and negative components. Experimental results on the segmentation of the kidneys from low-contrast DCE-MRI and on the segmentation of the ventricles from brain MRI's show how the approach is accurate in segmenting 2-D and 3-D data sets. The 2D results for the kidney segmentation have been validated by a radiologist and the 3D results of the ventricle segmentation have been validated with a geometrical phantom.

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