An accurate ventricular function quantification is important to support evaluation, diagnosis and prognosis of several cardiac pathologies. However, expert heart delineation, specifically for the right ventricle, is a time consuming task with high inter-and-intra observer variability. A fully automatic 3D+time heart segmentation framework is herein proposed for short-axis-cardiac MRI sequences. This approach estimates the heart using exclusively information from the sequence itself without tuning any parameters. The proposed framework uses a coarse-to-fine approach, which starts by localizing the heart via spatio-temporal analysis, followed by a segmentation of the basal heart that is then propagated to the apex by using a non-rigid-registration strategy. The obtained volume is then refined by estimating the ventricular muscle by locally searching a prior endocardium- pericardium intensity pattern. The proposed framework was applied to 48 patients datasets supplied by the organizers of the MICCAI 2012 Right Ventricle segmentation challenge. Results show the robustness, efficiency and competitiveness of the proposed method both in terms of accuracy and computational load.
An accurate right ventricular (RV) function quantification is important to support the evaluation, diagnosis and
prognosis of several cardiac pathologies and to complement the left ventricular function assessment. However,
expert RV delineation is a time consuming task with high inter-and-intra observer variability. In this paper
we present an automatic segmentation method of the RV in MR-cardiac sequences. Unlike atlas or multi-atlas
methods, this approach estimates the RV using exclusively information from the sequence itself. For so doing,
a spatio-temporal analysis segments the heart at the basal slice, segmentation that is then propagated to the
apex by using a non-rigid-registration strategy. The proposed approach achieves an average Dice Score of 0:79
evaluated with a set of 48 patients.
During the last decade, X-ray micro Computerized Tomography (CT) has become a conventional technique for the three-dimensional (3D) investigation of trabecular bone micro-architecture. Coupling micro-CT to synchrotron sources possesses significant advantages in terms of image quality and gives access to information on bone mineralization which is an important factor of bone quality. We present an overview of the investigation of bone using Synchrotron Radiation (SR) CT from the micro to the nano scale. We introduce two synchrotron CT systems developed at the ESRF based on SR parallel-beam micro-CT and magnified phase CT respectively, achieving down to submicrometric and nanometric spatial resolution. In the latter, by using phase retrieval prior to tomographic reconstruction, the system provides maps of the 3D refractive index distribution. Parallel-beam SR micro-CT has extensively been used for the analysis of trabecular or cortical bone in human or small animals with spatial resolution in the range [3-10] μm. However, the characterization of the bone properties at the cellular scale is also of major interest. At the micrometric scale, the shape, density and morphology of osteocyte lacunae can be studied on statistically representative volumes. At the nanometric scale, unprecedented 3D displays of the canaliculi network have been obtained on fields of views including a large number of interconnected osteocyte lacunae. Finally SR magnified phase CT provides a detailed analysis of the lacuno-canalicular network and in addition information on the organization of the collagen fibers. These findings open new perspectives for three-dimensional quantitative assessment of bone tissue at the cellular scale.
Measurement of the cortical thickness from 3D Magnetic Resonance Imaging (MRI) can aid diagnosis and
longitudinal studies of a wide range of neurodegenerative diseases. We estimate the cortical thickness using a
Laplacian approach whereby equipotentials analogous to layers of tissue are computed. The thickness is then
obtained using an Eulerian approach where partial differential equations (PDE) are solved, avoiding the explicit
tracing of trajectories along the streamlines gradient. This method has the advantage of being relatively fast
and insure unique correspondence points between the inner and outer boundaries of the cortex. The original
method is challenged when the thickness of the cortex is of the same order of magnitude as the image resolution
since partial volume (PV) effect is not taken into account at the gray matter (GM) boundaries. We propose
a novel way to take into account PV which improves substantially accuracy and robustness. We model PV by
computing a mixture of pure Gaussian probability distributions and use this estimate to initialize the cortical
thickness estimation. On synthetic phantoms experiments, the errors were divided by three while reproducibility
was improved when the same patients was scanned three consecutive times.
Conference Committee Involvement (4)
Image Processing
17 February 2025 | San Diego, California, United States
Image Processing
19 February 2024 | San Diego, California, United States
Image Processing
20 February 2023 | San Diego, California, United States
Tenth International Symposium on Medical Information Processing and Analysis
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