MESA: Complete approach for design and evaluation of segmentation methods using real and simulated tomographic images

et al. 2017

Med Biol Eng Comput

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Selective internal radiation therapy (SIRT) using Yttrium-90 loaded glass microspheres injected in the hepatic artery is an emerging, minimally invasive therapy of liver cancer. A personalized intervention can lead to high concentration dose in the tumor, while sparing the surrounding parenchyma. We propose a computational model for patient-specific simulation of entire hepatic arterial tree, based on liver, tumors, and arteries segmentation on patient's tomography. Segmentation of hepatic arteries down to a diameter of 0.5 mm is semi-automatically performed on 3D cone-beam CT angiography. The liver and tumors are extracted from CT-scan at portal phase by an active surface method. Once the images are registered through an automatic multimodal registration, extracted data are used to initialize a numerical model simulating liver vascular network. The model creates successive bifurcations from given principal vessels, observing Poiseuille's and matter conservation laws. Simulations provide a coherent reconstruction of global hepatic arterial tree until vessel diameter of 0.05 mm. Microspheres distribution under simple hypotheses is also quantified, depending on injection site. The patient-specific character of this model may allow a personalized numerical approximation of microspheres final distribution, opening the way to clinical optimization of catheter placement for tumor targeting.

Section: Liver Shape Segmentationmentioning

confidence: 99%

Towards a patient-specific hepatic arterial modeling for microspheres distribution optimization in SIRT protocol

Simoncini

Jurczuk

et al. 2017

Med Biol Eng Comput

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“…The algorithm was implemented using the MESA system [11]-a platform for designing and evaluation of the deformable model-based segmentation methods. MESA provides a template system for construction of active contours from exchangeable elements (i.e., models, energies and extensions), allowing an easy comparison of the proposed approach with other methods.…”

Section: Resultsmentioning

confidence: 99%

Toward Texture-Based 3D Level Set Image Segmentation

Image Processing and Communications Challenges 7

Boldak

Krȩtowski

2015

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This paper presents a three-dimensional level set-based image segmentation method. Instead of the typical image features, like intensity or edge information, the method uses texture feature analysis in order to be more applicable to image sets withs distinctive patterns. The current implementation makes use of a set of Grey Level Co-occurrence Matrix texture features that are generated and selected according to the characteristics of the initial region. The region is then deformed using the level set-based algorithm to cover the desired image area. The generation of the texture features and the level set surface deformation scheme are performed with graphics card hardware acceleration. The preliminary experiments, performed on synthetic data sets, show promising segmentation results.

“…The second stage level set method is also well suited for the GPU, as each separate position on the level set function can be recalculated independently. The The whole algorithm is implemented using the MESA system [43] -a platform for designing and evaluation of the deformable model-based segmentation methods. While the platform uses the Java language, the GPU-accelerated algorithms were written in C using OpenCL.…”

Section: Implementation Concernsmentioning

confidence: 99%

GPU-accelerated image segmentation based on level sets and multiple texture features

Krȩtowski

2020

Multimed Tools Appl

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In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.