Discrete Deformable Model Research Articles

Medial axis segmentation of cranial nerves using shape statistics-aware discrete deformable models.

We propose a segmentation methodology for brainstem cranial nerves using statistical shape model (SSM)-based deformable 3D contours from T2 MR images. We create shape models for ten pairs of cranial nerves. High-resolution T2 MR images are segmented for nerve centerline using a 1-Simplex discrete deformable 3D contour model. These segmented centerlines comprise training datasets for the shape model. Point correspondence for the training dataset is performed using an entropy-based energy minimization framework applied to particles located on the centerline curve. The shape information is incorporated into the 1-Simplex model by introducing a shape-based internal force, making the deformation stable against low resolution and image artifacts. The proposed method is validated through extensive experiments using both synthetic and patient MRI data. The robustness and stability of the proposed method are experimented using synthetic datasets. SSMs are constructed independently for ten pairs (CNIII-CNXII) of brainstem cranial nerves using ten non-pathological image datasets of the brainstem. The constructed ten SSMs are assessed in terms of compactness, specificity and generality. In order to quantify the error distances between segmented results and ground truths, two metrics are used: mean absolute shape distance (MASD) and Hausdorff distance (HD). MASD error using the proposed shape model is 0.19 ± 0.13 (mean ± std. deviation) mm and HD is 0.21mm which are sub-voxel accuracy given the input image resolution. This paper described a probabilistic digital atlas of the ten brainstem-attached cranial nerve pairs by incorporating a statistical shape model with the 1-Simplex deformable contour. The integration of shape information as a priori knowledge results in robust and accurate centerline segmentations from even low-resolution MRI data, which is essential in neurosurgical planning and simulations for accurate and robust 3D patient-specific models of critical tissues including cranial nerves.

3-D Active Meshes: Fast Discrete Deformable Models for Cell Tracking in 3-D Time-Lapse Microscopy

Variational deformable models have proven over the past decades a high efficiency for segmentation and tracking in 2-D sequences. Yet, their application to 3-D time-lapse images has been hampered by discretization issues, heavy computational loads and lack of proper user visualization and interaction, limiting their use for routine analysis of large data-sets. We propose here to address these limitations by reformulating the problem entirely in the discrete domain using 3-D active meshes, which express a surface as a discrete triangular mesh, and minimize the energy functional accordingly. By performing computations in the discrete domain, computational costs are drastically reduced, whilst the mesh formalism allows to benefit from real-time 3-D rendering and other GPU-based optimizations. Performance evaluations on both simulated and real biological data sets show that this novel framework outperforms current state-of-the-art methods, constituting a light and fast alternative to traditional variational models for segmentation and tracking applications.

A GPU framework for parallel segmentation of volumetric images using discrete deformable models

Despite the ability of current GPU processors to treat heavy parallel computation tasks, its use for solving medical image segmentation problems is still not fully exploited and remains challenging. A lot of difficulties may arise related to, for example, the different image modalities, noise and artifacts of source images, or the shape and appearance variability of the structures to segment. Motivated by practical problems of image segmentation in the medical field, we present in this paper a GPU framework based on explicit discrete deformable models, implemented over the NVidia CUDA architecture, aimed for the segmentation of volumetric images. The framework supports the segmentation in parallel of different volumetric structures as well as interaction during the segmentation process and real-time visualization of the intermediate results. Promising results in terms of accuracy and speed on a real segmentation experiment have demonstrated the usability of the system.

Discrete Deformable Model Guided by Partial Active Shape Model for TRUS Image Segmentation

Automatic prostate segmentation in transrectal ultrasound (TRUS) images is highly desired in many clinical applications. However, robust and automated prostate segmentation is challenging due to the low SNR in TRUS and the missing boundaries in shadow areas caused by calcifications or hyperdense prostate tissues. This paper presents a novel method of utilizing a priori shapes estimated from partial contours for segmenting the prostate. The proposed method is able to automatically extract prostate boundary from 2-D TRUS images without user interaction for shape correction in shadow areas. During the segmentation process, missing boundaries in shadow areas are estimated by using a partial active shape model, which takes partial contours as input but returns a complete shape estimation. With this shape guidance, an optimal search is performed by a discrete deformable model to minimize an energy functional for image segmentation, which is achieved efficiently by using dynamic programming. The segmentation of an image is executed in a multiresolution fashion from coarse to fine for robustness and computational efficiency. Promising segmentation results were demonstrated on 301 TRUS images grabbed from 19 patients with the average mean absolute distance error of 2.01 mm +/- 1.02 mm.

From MRI to anatomical simulation of the hip joint

AbstractThis paper describes a methodology for the simulation of musculoskeletal disorders. Our clinical study is related to osteoarthritis (OA) of the hip, a pathogenesis possibly due to impingements. These bone collisions lead to abnormal joint mechanics which is characterized by contact pressure and stress distribution upon the joint cartilages. The proposed methodology combines different approaches from modeling to simulation. The simulation is based on patient‐specific anatomical models, where acquisition modalities are noninvasive and flexible. Based on static magnetic resonance imaging (MRI) data, a discrete deformable models method is used for modeling the organs of the musculoskeletal system. Femoroacetabular movements are estimated using an optical motion capture system and are validated by a dynamic MRI analysis. To achieve accurate deformations, techniques to generate volumetric meshes are developed based on the medial axis (MA) information. Finally, a computationally efficient fast functional joint model is used to simulate the mechanical behavior of the soft tissues. The goal of such a simulation is to allow the investigation of the relevant contact and cartilages deformation under movement, which can be useful for diagnosis, pre‐ or post‐operative planning and training. This will benefit further developments in surgical techniques and minimally invasive procedures. Copyright © 2008 John Wiley & Sons, Ltd.